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MuiButton-root MuiButton-text MuiButton-textPrimary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorPrimary MuiButton-root MuiButton-text MuiButton-textPrimary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorPrimary sc-d74559df-0 hctdIg css-ak4akz" tabindex="0" type="button" href="" target="_self"><span class="sc-b323b31-0 btXZGI button-label" display="inline-flex"><div width="16.5px" height="13px" class="sc-6293d692-0 eerUnR"><span display="block" height="2px" width="100%" opacity="1" transform="rotate(0)" class="sc-b323b31-0 fAyXOw"></span><span display="block" height="2px" width="100%" opacity="1" class="sc-b323b31-0 jYoQJr"></span><span display="block" height="2px" width="100%" opacity="1" transform="rotate(0)" class="sc-b323b31-0 bzvkpX"></span></div></span></button></div><ul width="100%" height="0px" class="sc-ace17a57-0 sc-f5e67a3c-2 girkTs czlhnZ"><div display="none" class="sc-ace17a57-0 bCtuFU"><a rel="" target="" width="100%" display="flex" href="/" 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class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060023-speech-to-speech.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dVEgqo">Voice Agent API</span><span font-size="textXs" class="sc-b323b31-0 sc-3618c394-14 lnJhVG cFsKfJ">For real-time AI Agents</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC bGaVbc"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/product/text-to-speech"><div class="sc-ace17a57-0 gCajMv"><div class="sc-ace17a57-0 sc-3618c394-4 ftcNci bpvjgx"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060055-text-to-speech.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dVEgqo">Text to Speech API</span><span font-size="textXs" class="sc-b323b31-0 sc-3618c394-14 lnJhVG cFsKfJ">Responsive, natural-sounding voices</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC bGaVbc"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/product/speech-to-text"><div class="sc-ace17a57-0 gCajMv"><div class="sc-ace17a57-0 sc-3618c394-4 ftcNci bpvjgx"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060077-speech-to-text.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dVEgqo">Speech to Text API</span><span font-size="textXs" class="sc-b323b31-0 sc-3618c394-14 lnJhVG cFsKfJ">Unmatched accuracy, speed & cost</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC bGaVbc"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/product/audio-intelligence"><div class="sc-ace17a57-0 gCajMv"><div class="sc-ace17a57-0 sc-3618c394-4 ftcNci bpvjgx"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060103-brain-ai.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dVEgqo">Audio Intelligence API</span><span font-size="textXs" class="sc-b323b31-0 sc-3618c394-14 lnJhVG cFsKfJ">Powered by AI Language models</span></div></div></a></div></div></div></div></div></div><div width="calc(100%)" class="sc-ace17a57-0 sc-3618c394-6 hiDOHk juEUpi"><div width="100%" class="sc-ace17a57-0 lePzZD"><span display="block" height="36px" class="sc-b323b31-0 ilnsGr">Announcements</span><style data-emotion="css 1y3y2u2">.css-1y3y2u2{font-family:var(--font-inter, sans-serif);font-size:1.0446428571428572rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:4px 5px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms 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MuiButton-colorSecondary MuiButton-root MuiButton-text MuiButton-textSecondary MuiButton-sizeSmall MuiButton-textSizeSmall MuiButton-colorSecondary sc-d74559df-0 fqnlbI sc-6e03a9e4-0 iumJHL sc-3618c394-0 gxydGy css-6n9fb7" tabindex="0" type="button" __typename="ComponentCallToActionRecord" id="Xho9X9_YQj2HBRVr87BG0g" href="/learn?type=Article&page=1&category=Announcements"><span class="sc-b323b31-0 fcPowg button-label" display="inline-flex"><span>View more</span></span><style data-emotion="css kcxyz4">.css-kcxyz4{display:inherit;margin-right:-2px;margin-left:8px;}.css-kcxyz4>*:nth-of-type(1){font-size:18px;}</style><span class="MuiButton-icon MuiButton-endIcon MuiButton-iconSizeSmall css-kcxyz4"><div width="fit-content" class="sc-ace17a57-0 ddioGD"><span display="inline-flex" class="sc-b323b31-0 bZmEHx"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 448 512" height="16" width="16" xmlns="http://www.w3.org/2000/svg"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"></path></svg></span></div></span></a></div><div class="sc-ace17a57-0 gzWdaa"><a rel="" target="" display="-webkit-box" height="100%" width="100%" class="sc-e69ac761-0 ecplwP sc-2850a57f-3 cPKdIq" href="/learn/introducing-nova-3-speech-to-text-api"><div width="100%" height="100%" id="e1wy33mNTlGk8-WqDH28_Q" class="sc-ace17a57-0 sc-2850a57f-1 fJWxEy ggobuO"><div width="100%" height="150px" class="sc-6293d692-0 eWcIvK"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK image-wrapper" width="100%" height="100%"><img alt="Introducing Nova-3: Setting a New Standard for AI-Driven Speech-to-Text" loading="lazy" width="590" height="259" decoding="async" data-nimg="1" class="sc-2850a57f-2 dSsmQU" style="color:transparent;object-fit:cover" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1740086862-2502-nova-3-blog-header.jpg%3Fauto%3Dformat%26w%3D590&w=640&q=75 1x, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1740086862-2502-nova-3-blog-header.jpg%3Fauto%3Dformat%26w%3D590&w=1200&q=75 2x" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1740086862-2502-nova-3-blog-header.jpg%3Fauto%3Dformat%26w%3D590&w=1200&q=75"/></div></div><div class="sc-ace17a57-0 biWqHE"><span font-weight="semiBold" class="sc-b323b31-0 iNRMrC"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 320 512" color="#ffffff" class="sc-2850a57f-0 Tcnof" style="color:#ffffff" height="12" width="12" xmlns="http://www.w3.org/2000/svg"><path d="M285.476 272.971L91.132 467.314c-9.373 9.373-24.569 9.373-33.941 0l-22.667-22.667c-9.357-9.357-9.375-24.522-.04-33.901L188.505 256 34.484 101.255c-9.335-9.379-9.317-24.544.04-33.901l22.667-22.667c9.373-9.373 24.569-9.373 33.941 0L285.475 239.03c9.373 9.372 9.373 24.568.001 33.941z"></path></svg>Introducing Nova-3: Setting a New Standard for AI-Driven Speech-to-Text</span><span class="sc-b323b31-0 jUNaqu">Published on 02/12/25</span></div></div></a></div></div></div></div></li><li height="fit-content" width="100%" tabindex="0" class="sc-6293d692-0 hEIHLV"><div width="100%" class="sc-ace17a57-0 gbwWMz"><div height="fit-content" width="100%" class="sc-ace17a57-0 caCSrG"><span font-size="textXl" font-weight="medium" display="block" cursor="pointer" width="100%" class="sc-b323b31-0 kzoxAi">Solutions</span><div transform="rotateX(0deg)" class="sc-ace17a57-0 fxtsZN"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 448 512" color="#A9A9AD" style="color:#A9A9AD" height="12" width="12" xmlns="http://www.w3.org/2000/svg"><path d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></div></div></div><div pointer-events="none" class="sc-6293d692-0 sc-610ba7e0-0 fqJJID gkvAJG"><div class="sc-ace17a57-0 iIvmda"><div class="sc-ace17a57-0 fXUJvn"><div class="sc-ace17a57-0 jbARwL"><div class="sc-ace17a57-0 exshrD"><div class="sc-ace17a57-0 gYFcFc"><span display="block" width="auto" class="sc-b323b31-0 gIJLbd">Use Cases</span></div><div class="sc-ace17a57-0 eTCxuy"><div class="sc-ace17a57-0 ldsYPN"><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/contact-centers"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060222-customer-service.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Contact Centers</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/medical-transcription"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060247-medical.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Medical Transcription</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/deepgram-for-voicebots-and-chatbots"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060265-conversational-ai.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Conversational AI</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/speech-analytics"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060282-speech-analytics.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Speech Analytics</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/media-transcription"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060298-media.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Media Transcription</span></div></div></a></div></div></div></div><div class="sc-ace17a57-0 bcBFEg"><a rel="" target="" class="sc-e69ac761-0 bkHGsI sc-3618c394-11 cTfqJK" href="/customers"><div class="sc-ace17a57-0 ldePaW"><div width="28px" height="28px" class="sc-6293d692-0 sc-3618c394-1 ipShUF kXFUqt"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK image-wrapper" width="100%" height="100%"><img alt="Customers" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" class="sc-3618c394-8 jzJMzZ" style="color:transparent;object-fit:cover" src="https://www.datocms-assets.com/96965/1728060368-user.svg"/></div></div><span font-size="textSm" class="sc-b323b31-0 sc-3618c394-15 fmcyDO gpxdfo">Customers</span></div></a><a rel="" target="" class="sc-e69ac761-0 bkHGsI sc-3618c394-11 cTfqJK" href="/partners"><div class="sc-ace17a57-0 ldePaW"><div width="28px" height="28px" class="sc-6293d692-0 sc-3618c394-1 ipShUF kXFUqt"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK image-wrapper" width="100%" height="100%"><img alt="Partners" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" class="sc-3618c394-8 jzJMzZ" style="color:transparent;object-fit:cover" src="https://www.datocms-assets.com/96965/1728060385-people-connect.svg"/></div></div><span font-size="textSm" class="sc-b323b31-0 sc-3618c394-15 fmcyDO gpxdfo">Partners</span></div></a><a rel="" target="" class="sc-e69ac761-0 bkHGsI sc-3618c394-11 cTfqJK" 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MuiButton-colorSecondary MuiButton-root MuiButton-text MuiButton-textSecondary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorSecondary sc-d74559df-0 cIekQB sc-84b76e46-0 sc-f5e67a3c-0 jroYld css-1vy4m6x" tabindex="0" type="button" href="https://console.deepgram.com/" target="_self" id="108492178" __typename="ComponentCallToActionRecord"><span class="sc-b323b31-0 btXZGI button-label" display="inline-flex"><span>Log In</span></span></a><style data-emotion="css 134mfhg">.css-134mfhg{font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 16px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 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rgba(0,0,0,0.12);}.css-40xoaf.Mui-disabled{color:rgba(0, 0, 0, 0.26);box-shadow:none;background-color:rgba(0, 0, 0, 0.12);}.css-40xoaf::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-40xoaf::before{background:linear-gradient(to right, #008fc1, #00f099, #008fc1, #00f099);-webkit-background-size:300% 100%;background-size:300% 100%;}.css-40xoaf:hover{color:#0B0B0C;box-shadow:0px 1px 2px rgba(38, 44, 52, 0.05);background:#ffffff;}.css-40xoaf:hover::before{opacity:1;-webkit-background-position:100% 0;background-position:100% 0;}.css-40xoaf:focus{background:#ffffff;box-shadow:0px 1px 2px rgba(16, 24, 40, 0.05),0px 0px 0px 2px #687EF7;border-color:#ffffff;}.css-40xoaf.Mui-disabled{background:#2C2C33;color:#A9A9AD;}</style><style data-emotion="css ubnyan">.css-ubnyan{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 16px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:rgba(0, 0, 0, 0.87);background-color:#00E062;box-shadow:0px 3px 1px -2px rgba(0,0,0,0.2),0px 2px 2px 0px rgba(0,0,0,0.14),0px 1px 5px 0px rgba(0,0,0,0.12);min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;background-color:#ffffff;color:#0B0B0C;border:1px solid #ffffff;}.css-ubnyan::-moz-focus-inner{border-style:none;}.css-ubnyan.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ubnyan{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ubnyan:hover{-webkit-text-decoration:none;text-decoration:none;background-color:#00BB49;box-shadow:0px 2px 4px -1px rgba(0,0,0,0.2),0px 4px 5px 0px rgba(0,0,0,0.14),0px 1px 10px 0px rgba(0,0,0,0.12);}@media (hover: none){.css-ubnyan:hover{background-color:#00E062;}}.css-ubnyan:active{box-shadow:0px 5px 5px -3px rgba(0,0,0,0.2),0px 8px 10px 1px rgba(0,0,0,0.14),0px 3px 14px 2px rgba(0,0,0,0.12);}.css-ubnyan.Mui-focusVisible{box-shadow:0px 3px 5px -1px rgba(0,0,0,0.2),0px 6px 10px 0px rgba(0,0,0,0.14),0px 1px 18px 0px rgba(0,0,0,0.12);}.css-ubnyan.Mui-disabled{color:rgba(0, 0, 0, 0.26);box-shadow:none;background-color:rgba(0, 0, 0, 0.12);}.css-ubnyan::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ubnyan::before{background:linear-gradient(to right, #008fc1, #00f099, #008fc1, #00f099);-webkit-background-size:300% 100%;background-size:300% 100%;}.css-ubnyan:hover{color:#0B0B0C;box-shadow:0px 1px 2px rgba(38, 44, 52, 0.05);background:#ffffff;}.css-ubnyan:hover::before{opacity:1;-webkit-background-position:100% 0;background-position:100% 0;}.css-ubnyan:focus{background:#ffffff;box-shadow:0px 1px 2px rgba(16, 24, 40, 0.05),0px 0px 0px 2px #687EF7;border-color:#ffffff;}.css-ubnyan.Mui-disabled{background:#2C2C33;color:#A9A9AD;}</style><a class="MuiButtonBase-root MuiButton-root MuiButton-contained MuiButton-containedPrimary MuiButton-sizeMedium MuiButton-containedSizeMedium MuiButton-colorPrimary MuiButton-root MuiButton-contained MuiButton-containedPrimary MuiButton-sizeMedium MuiButton-containedSizeMedium MuiButton-colorPrimary sc-d74559df-0 cIekQB sc-84b76e46-0 etnILU sc-f5e67a3c-0 jroYeL css-ubnyan" tabindex="0" type="button" href="https://console.deepgram.com/signup" target="_self" id="108492199" __typename="ComponentCallToActionRecord"><span class="sc-b323b31-0 btXZGI button-label" display="inline-flex"><span>Sign Up</span></span></a></div></ul></div></div></div><div display="none" id="nav-overlay" height="0" class="sc-ace17a57-0 jUgQHG"></div></div></nav></div></header><section id="" width="100%" class="sc-ace17a57-0 sc-b22001da-2 iLVWKa eAcnIZ"><div width="100%" class="sc-6293d692-0 chAbBM"><div class="sc-ace17a57-0 doHfou"><a rel="" target="" href="/ai-glossary" class="sc-e69ac761-0 fobbYG"><h2 font-size="displayLg" font-family="primaryFont" class="sc-4555ca6a-0 htUWGk">AI Glossary</h2></a></div></div></section><section id="" width="100%" class="sc-ace17a57-0 sc-b22001da-2 hMGZHr dJJSTR"><div width="100%" class="sc-6293d692-0 chAbBM"><div height="100%" width="100%" class="sc-6293d692-0 gKLLRj"><div height="100%" width="100%" class="sc-662dedcb-0 qCkfD"><div class="sc-6293d692-0 csGpzP"><div class="sc-ace17a57-0 hhtFqm glossary-content-wrapper"><div class="sc-ace17a57-0 btkfVN"><div class="sc-ace17a57-0 sc-a48d0877-0 dJCIVv zIKHU"><h1 font-size="displaySm" letter-spacing="neg2" font-weight="semiBold" font-family="primaryFont" class="sc-b323b31-0 JYZsj">Keyphrase Extraction</h1><div class="sc-ace17a57-0 fJkbQy"><p font-family="bodyFont" class="sc-5159831f-0 GBftN">Keyphrase extraction is crucial for analyzing customer reviews, understanding sentiments, and spotting emerging trends. It's difficult, demanding attention to linguistic details, context, and document structure. Yet, it plays a vital role in information retrieval and NLP.</p></div></div></div><div class="sc-ace17a57-0 sc-4e888a2-0 hfDdWp cifBqP"><p class="sc-5159831f-0">Keyphrase extraction in <a href="https://deepgram.com/ai-glossary/natural-language-processing"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">natural language processing (NLP)</span></a> involves pulling out the most important phrases from a document and capturing their essence. This technique facilitates document similarity checks and enhances search efficiency. Matching user queries with extracted phrases speeds up retrieval, especially in large databases.</p><p class="sc-5159831f-0">In business, keyphrase extraction is crucial for analyzing customer reviews, understanding sentiments, and spotting emerging trends. It's difficult, demanding attention to linguistic details, context, and document structure. Yet, it plays a vital role in information retrieval and NLP.</p><h2 id="understanding-keyphrase-extraction" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Understanding Keyphrase Extraction</h2><p class="sc-5159831f-0">The keyphrase extraction process is divided into two stages: </p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Extraction of Candidate Phrases</span></em>: It involves studying how words are used and the way documents are written, looking for possible phrases based on specific rules, like how often they appear or how important they are, and then using either manual or automated methods, such as <a href="https://en.wikipedia.org/wiki/Regular_expression"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">regular expressions</span></a>, to pull out the important phrases. These hard-coded extracted phrases are termed <span font-weight="bold" class="sc-b323b31-0 caUwZV">candidate phrases</span>.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Ranking of Candidate Phrases: </span>Once candidate phrases have been identified, the ranking is determined based on their relevance to a text document of interest. The candidate phrases that rank the highest are keyphrases for that document. This ranking process is typically accomplished using specialized algorithms like….. </p></li></li></ul><p class="sc-5159831f-0">First, the candidate phrases are converted into numerical vectors in a process called<a href="https://en.wikipedia.org/wiki/Word_embedding"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM"> word embedding</span></a>. After that, we take the entire document, let's say an article on artificial intelligence, and represent it as another vector. Finally, we compare the vectors of the candidate phrases with the vector of the entire document. This comparison helps us understand how closely these phrases relate to the content of the article, allowing us to assess their similarity and rank their importance within the context of the document.</p><h3 id="keyphrase-extraction-compared-to-other-nlp-techniques" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Keyphrase Extraction compared to other NLP Techniques</h3><p class="sc-5159831f-0">Keyphrase extraction shares similarities with other NLP techniques. In this section, we will break down the similarities and differences.</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Keyword Extraction: </span></em>Keyword and keyphrase extraction are often confused but have distinct goals. Keyword extraction aims to extract important words from a document. In contrast, keyphrase extraction targets grouped words that form phrases. Think of keyword extraction as a part of keyphrase extraction—they use similar techniques.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Text Summarization: </span>Text summarization is an NLP technique where a lengthy document is condensed while keeping its meaning intact. Keyphrase extraction helps in summarization by ensuring essential phrases are included, irrespective of the document's size</p></li></li></ul><p class="sc-5159831f-0">Both techniques differ in their usage and implementation. Summaries are often generated by extracting key sentences from a document. This technique, known as <a href="https://blog.paperspace.com/extractive-and-abstractive-summarization-techniques/#extractive-summarization"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">extractive text summarization</span></a>, is similar to those used for extracting phrases. However, more advanced NLP techniques are now used to generate summaries in practice.</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Information Extraction: </span>Information extraction involves retrieving organized information like dates, location or any relevant information in a text document. In contrast, keyphrase extraction is used to identify key terms or phrases that represent the themes of a document. Since text is unstructured, we use information extraction to pull out useful details in a structured way. Techniques like Named Entity Recognition (<a href="https://deepgram.com/ai-glossary/natural-language-processing#named-entity-recognition"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">NER</span></a>) are valuable for information extraction tasks.</p></li></li></ul><h2 id="techniques-for-keyphrase-extraction" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Techniques for Keyphrase Extraction</h2><p class="sc-5159831f-0">Keyphrase Extraction Techniques can be categorized into two groups:</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0">Supervised techniques.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0">Unsupervised techniques.</p></li></li></ul><h3 id="supervised-keyphrase-extraction-techniques" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Supervised Keyphrase Extraction Techniques</h3><p class="sc-5159831f-0">In this method, you train a model with a dataset of labeled keyphrases for a particular domain. For instance, we can have a dataset of keyphrases for a specific domain, like business. When extracting keyphrases from a new document, the model decides whether each candidate phrase is a keyphrase.</p><p class="sc-5159831f-0">While supervised techniques excel in their domain, creating the training dataset is time-consuming. They may perform poorly in different subjects due to specific training data characteristics.</p><p class="sc-5159831f-0">In supervised keyphrase extraction, tasks can be either classification (deciding if a phrase is a keyphrase) or ranking (assigning ranks to phrases). The <a href="https://en.wikipedia.org/wiki/Ranking_SVM"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Ranking SVM</span></a>, using a support vector machine, is an example of a model for ranking tasks.</p><h3 id="unsupervised-keyphrase-extraction-techniques" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Unsupervised Keyphrase Extraction Techniques</h3><p class="sc-5159831f-0">Unsupervised keyphrase extraction techniques do not rely on a pre-existing dataset to train a model for extraction. Instead, they use methods ranging from analyzing a text's linguistic properties to utilizing language models for extraction.</p><h4 id="frequencybased-method-tfidf" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">Frequency-based method: TF-IDF</h4><p class="sc-5159831f-0">This simple and effective technique extracts phrases by focusing on their frequency in a document. It identifies commonly occurring word groups, assuming that important phrases will be repeated several times in the document.</p><p class="sc-5159831f-0">Term Frequency (<a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf#:~:text=%EF%BF%BD-,Term%20frequency,-%5Bedit%5D"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">TF</span></a>) is one such approach that is popularly used, especially in keyword extraction. It involves extracting the most frequently occurring word in the document. However, it can also be employed in keyphrase extraction by considering <a href="https://en.wikipedia.org/wiki/N-gram"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">n-grams</span></a> greater than 1.</p><p class="sc-5159831f-0">Inverse Document Frequency (<a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf#:~:text=%7D-,Inverse%20document%20frequency,-%5Bedit%5D"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">IDF</span></a>) complements TF by assessing term rarity across documents. It can also analyze multiple paragraphs in one document. The phrases with the highest IDF in the document are considered keyphrases.</p><p class="sc-5159831f-0">While frequency-based approaches work in some cases, they have limitations. They might catch frequently occurring word sequences, but not all repetitions form meaningful phrases. </p><p class="sc-5159831f-0">Frequency-based approaches can be effective in some contexts, but they have limitations that make them less reliable in certain situations. For example, while these approaches may identify frequently occurring word sequences, not all repetitions are meaningful phrases.</p><h4 id="linguistics-method-partofspeech-pos-tagging" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">Linguistics method: Part-of-speech (POS) tagging</h4><p class="sc-5159831f-0">This technique involves breaking down the text into words and labeling each word with its <a href="https://en.wikipedia.org/wiki/Part_of_speech#:~:text=In%20grammar%2C%20a%20part%20of,that%20have%20similar%20grammatical%20properties."><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">part of speech (POS)</span></a>. Then, using predefined rules and patterns (regular expression), phrases are extracted based on linguistic properties like nouns, verbs, adjectives, etc., using regular expressions. </p><p class="sc-5159831f-0">For instance, we can use a pattern to detect phrases without a subject and predicate. Regular expressions are also handy for spotting nouns, adjectives, and verb phrases.</p><p class="sc-5159831f-0"><a href="https://en.wikipedia.org/wiki/Part-of-speech_tagging"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">POS tagging</span></a> is often the first step to finding potential phrases in a document. These phrases then go through a more advanced process to identify the actual keyphrases.</p><h4 id="vector-embeddings-word2vec-doc2vec-and-glove" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">Vector Embeddings: Word2Vec, Doc2Vec and Glove</h4><p class="sc-5159831f-0">Embedding is another technique employed for keyphrase extraction. This process involves converting potential keyphrases into vector representations and then comparing them with the vector representation of the document. Using vector embedding for NLP was initially introduced with the <a href="https://arxiv.org/abs/1301.3781"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Word2Vec</span></a> algorithm, which proposed that words with similar meanings have similar vector representations. </p><p class="sc-5159831f-0">Later developments, such as <a href="https://medium.com/wisio/a-gentle-introduction-to-doc2vec-db3e8c0cce5e"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Doc2Vec</span></a>, extended this concept to entire documents. GloVe (<a href="https://en.wikipedia.org/wiki/GloVe"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Glove Vectors</span></a>), on the other hand, applies the same principles as Word2Vec but with a simple difference. GloVe uses global <a href="https://en.wikipedia.org/wiki/Co-occurrence"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">co-occurrence statistics</span></a> across the entire corpus, while Word2Vec focuses on local context.</p><h2 id="evaluation-metrics-for-keyphrase-extraction" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Evaluation Metrics for Keyphrase Extraction</h2><p class="sc-5159831f-0">When evaluating keyphrase extraction algorithms, one common approach is treating the task as a binary classification problem, where the algorithm predicts whether a candidate phrase is a keyphrase. But not all keyphrases are equally relevant to the text document. Some keyphrases may have a stronger relationship with the document content than others. Therefore, it's essential to use evaluation metrics that consider the ranking of keyphrases.</p><p class="sc-5159831f-0">This leads us to our two categories of metrics for Keyphrase Extraction: </p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0">Traditional Metrics </p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0">Rank-based Metrics.</p></li></li></ul><h3 id="traditional-metrics" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Traditional Metrics</h3><p class="sc-5159831f-0">These metrics are commonly employed in classification tasks. They include precision, recall, and the F1 score. Here's how you use them for keyphrase extraction:</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Precision: </span><a href="https://deepgram.com/ai-glossary/precision-and-recall"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Precision</span></a> for keyphrase extraction is the proportion of correctly identified keyphrases extracted by the algorithm. In other words, it measures the accuracy of the extracted keyphrases.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Recall:</span></em><span font-weight="bold" class="sc-b323b31-0 caUwZV"> </span><a href="https://deepgram.com/ai-glossary/precision-and-recall"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Recall</span></a> is the proportion of correctly identified keyphrases among all the relevant keyphrases in the document or corpus. It measures the completeness of the keyphrase extraction process.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">F1 Score:</span></em> The <a href="https://deepgram.com/ai-glossary/f1-score-machine-learning"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">F1 score</span></a> combines precision and recall into one metric, using the harmonic mean. In keyphrase extraction, we're not evaluating the model across all possible categories but focusing on its accuracy in extracting a set number of keyphrases from a document.</p></li></li></ul><p class="sc-5159831f-0">These evaluation metrics are similar to top-k classification tasks, where the model is assessed on predicting the top-k classes with the highest confidence scores. For keyphrase extraction, metrics like <span font-weight="bold" class="sc-b323b31-0 caUwZV">precision@k</span>, <span font-weight="bold" class="sc-b323b31-0 caUwZV">recall@k</span>, and <span font-weight="bold" class="sc-b323b31-0 caUwZV">F1 score@k</span> specifically evaluate the model's effectiveness in identifying the most relevant keyphrases within a given limit, resembling top-k classification scenarios.</p><h3 id="rankbased-metric" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Rank-Based Metric</h3><p class="sc-5159831f-0">These metrics treat the keyphrase extraction task as a ranking problem, evaluating each extracted keyphrase based on its relevance to the document content.</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Mean Reciprocal Rank (MRR): </span>Mean Reciprocal Rank (<a href="https://en.wikipedia.org/wiki/Mean_reciprocal_rank"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">MRR</span></a>) is used to evaluate the effectiveness of ranking the extracted keyphrases. It measures the average quality of the ranking by assessing the reciprocal of the rank of the first correctly extracted keyphrase in the list of candidate keyphrases. In other words, MRR quantifies how quickly the algorithm can identify relevant keyphrases, with a higher MRR value indicating better performance. </p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Mean Average Precision (MAP):</span> Mean Average Precision (<a href="https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision:~:text=precision.%5B15%5D-,Mean%20average%20precision,-%5Bedit%5D"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">MAP</span></a>) is used to evaluate the overall ranking quality produced by the extraction algorithm across multiple documents. It calculates the average precision for each document and then computes the mean of these average precisions.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Normalized Discounted Cumulative Gain (nDCG): </span></em>Normalized Discounted Cumulative Gain (<a href="https://en.wikipedia.org/wiki/Discounted_cumulative_gain"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">nDCG</span></a>) assesses how well the extracted keyphrases are ranked, considering their relevance and position in the list. It calculates the total gain by adding the relevance scores of keyphrases, and adjusting for their position. The ideal score then normalizes the gain to get the nDCG.</p></li></li></ul><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="ZHaO8fOcRLmrH9udSjy3QQ"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1504" height="972" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1504 972'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718529518-screen-shot-2024-06-16-at-2-18-35-am.png&w=3840&q=75"/></div><div font-size="textSm" font-style="italic" class="sc-6293d692-0 jgEMAk"></div></div></p><h2 id="challenges-in-keyphrase-extraction" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Challenges in Keyphrase Extraction</h2><p class="sc-5159831f-0">Keyphrase extraction encounters several challenges that can impede the performance of even state-of-the-art algorithms. Here are a few of these challenges:</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Loss of Context:</span></em><span font-weight="bold" class="sc-b323b31-0 caUwZV"> </span>Context is often lost once a keyphrase is extracted from a document. It becomes challenging to discern the relevance of a phrase when it is isolated from other words that provide additional context. As a result, the extracted phrase may be erroneously ranked lower than its actual relevance.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Ambiguity due to Polysemy:</span></em> <a href="https://en.wikipedia.org/wiki/Polysemy"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Polysemy</span></a> is when a phrase has multiple meanings. Ambiguity arises due to this. A keyphrase algorithm must navigate through all of this in order to correctly identify and extract the most contextually relevant keyphrase. </p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Adaptation to Different Languages: </span></em>A challenge in keyphrase extraction is smoothly transferring learned phrases between languages. Since each language has its own rules, techniques like POS tagging don't work well across languages. Even language embedding models need retraining. This requires specific methods for each language and constant reassessment to ensure effectiveness across different languages.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Adaptation to a new domain: </span></em>This challenge is common in supervised keyphrase extraction models. They find it hard to apply what they learn from one knowledge domain to another because each domain has unique keyphrases. While unsupervised models may help with domain-specific issues, past <a href="https://aclanthology.org/P17-1054/"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">research</span></a> shows that supervised models often perform better than unsupervised ones.</p></li></li></ul><h2 id="realworld-applications" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Real-world Applications</h2><p class="sc-5159831f-0">Keyphrase extraction is applied to various industries. The following are a couple of applications:</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Search Engine Optimization (SEO) for Digital Content: </span></em>Keyphrases are essential for enhancing by improving how content ranks on search engines. A keyphrase extraction algorithm helps find relevant keyphrases, which can be added to metadata, used as alternative text for images, and inform ad creation on platforms like <a href="https://ads.google.com/home/"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Google Ads</span></a>. Utilizing keyphrases improves the SEO performance of content.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><em><span font-weight="bold" class="sc-b323b31-0 caUwZV">Business Intelligence through Customer Feedback Analysis: </span></em>Keyphrase extraction helps businesses understand customer feedback from various sources like social media, surveys, and reviews. By analyzing these keyphrases, businesses can learn about customer sentiments and preferences. This helps them identify trends and patterns in feedback, revealing what aspects of their products or services are most important to customers.</p></li></li></ul><h2 id="conclusion" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Conclusion</h2><p class="sc-5159831f-0">In summary, keyphrase extraction is an important part of understanding written content. It's versatile, helping in summarizing, trend analysis, and decoding customer feedback, among other use cases. 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href="/ai-glossary/grounding" class="sc-e69ac761-0 kmKRMV">Grounding</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feedforward-neural-network" class="sc-e69ac761-0 kmKRMV">Feedforward Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bert" class="sc-e69ac761-0 kmKRMV">BERT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-boosting-machines" class="sc-e69ac761-0 kmKRMV">Gradient Boosting Machines (GBMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/retrieval-augmented-generation" class="sc-e69ac761-0 kmKRMV">Retrieval-Augmented Generation (RAG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/perceptron" class="sc-e69ac761-0 kmKRMV">Perceptron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/overfitting-underfitting" class="sc-e69ac761-0 kmKRMV">Overfitting and Underfitting</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning" class="sc-e69ac761-0 kmKRMV">Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/large-language-model" class="sc-e69ac761-0 kmKRMV">Large Language Model (LLM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/graphics-processing-unit-gpu" class="sc-e69ac761-0 kmKRMV">Graphics Processing Unit (GPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/diffusion-model" class="sc-e69ac761-0 kmKRMV">Diffusion Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/classification" class="sc-e69ac761-0 kmKRMV">Classification</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensor-processing-unit-tpu" class="sc-e69ac761-0 kmKRMV">Tensor Processing Unit (TPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-processing" class="sc-e69ac761-0 kmKRMV">Natural Language Processing (NLP)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/google-bard" class="sc-e69ac761-0 kmKRMV">Google's Bard</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-whisper" class="sc-e69ac761-0 kmKRMV">OpenAI Whisper</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/sequence-modeling" class="sc-e69ac761-0 kmKRMV">Sequence Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/precision-and-recall" class="sc-e69ac761-0 kmKRMV">Precision and Recall</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-kernel" class="sc-e69ac761-0 kmKRMV">Semantic Kernel</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fine-tuning-deep-learning" class="sc-e69ac761-0 kmKRMV">Fine Tuning in Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-scaling" class="sc-e69ac761-0 kmKRMV">Gradient Scaling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/alphago-zero" class="sc-e69ac761-0 kmKRMV">AlphaGo Zero</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-map" class="sc-e69ac761-0 kmKRMV">Cognitive Map</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keyphrase-extraction" class="sc-e69ac761-0 iUnKGS">Keyphrase Extraction</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-aI-models-and-modalities" class="sc-e69ac761-0 kmKRMV">Multimodal AI Models and Modalities</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hidden-markov-models" class="sc-e69ac761-0 kmKRMV">Hidden Markov Models (HMMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-hardware" class="sc-e69ac761-0 kmKRMV">AI Hardware</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deep-learning" class="sc-e69ac761-0 kmKRMV">Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-generation" class="sc-e69ac761-0 kmKRMV">Natural Language Generation (NLG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-understanding" class="sc-e69ac761-0 kmKRMV">Natural Language Understanding (NLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tokenization" class="sc-e69ac761-0 kmKRMV">Tokenization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/word-embeddings" class="sc-e69ac761-0 kmKRMV">Word Embeddings</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-and-finance" class="sc-e69ac761-0 kmKRMV">AI and Finance</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/alphago" class="sc-e69ac761-0 kmKRMV">AlphaGo</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-recommendation-algorithms" class="sc-e69ac761-0 kmKRMV">AI Recommendation Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/binary-classification-ai" class="sc-e69ac761-0 kmKRMV">Binary Classification AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-generated-music" class="sc-e69ac761-0 kmKRMV">AI Generated Music</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neuralink" class="sc-e69ac761-0 kmKRMV">Neuralink</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-video-generation" class="sc-e69ac761-0 kmKRMV">AI Video Generation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-sora" class="sc-e69ac761-0 kmKRMV">OpenAI Sora</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hooke-jeeves-algorithm" class="sc-e69ac761-0 kmKRMV">Hooke-Jeeves Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mamba" class="sc-e69ac761-0 kmKRMV">Mamba</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/central-processing-unit-cpu" class="sc-e69ac761-0 kmKRMV">Central Processing Unit (CPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-ai" class="sc-e69ac761-0 kmKRMV">Generative AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/representation-learning" class="sc-e69ac761-0 kmKRMV">Representation Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-in-customer-service" class="sc-e69ac761-0 kmKRMV">AI in Customer Service</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conditional-variational-autoencoders" class="sc-e69ac761-0 kmKRMV">Conditional Variational Autoencoders</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conversational-ai" class="sc-e69ac761-0 kmKRMV">Conversational AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/packages-glossary" class="sc-e69ac761-0 kmKRMV">Packages</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/models" class="sc-e69ac761-0 kmKRMV">Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fundamentals" class="sc-e69ac761-0 kmKRMV">Fundamentals</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/datasets" class="sc-e69ac761-0 kmKRMV">Datasets</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/techniques" class="sc-e69ac761-0 kmKRMV">Techniques</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-lifecycle-management" class="sc-e69ac761-0 kmKRMV">AI Lifecycle Management</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-literacy" class="sc-e69ac761-0 kmKRMV">AI Literacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-monitoring" class="sc-e69ac761-0 kmKRMV">AI Monitoring</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-oversight" class="sc-e69ac761-0 kmKRMV">AI Oversight</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-privacy" class="sc-e69ac761-0 kmKRMV">AI Privacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-prototyping" class="sc-e69ac761-0 kmKRMV">AI Prototyping</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-regulation" class="sc-e69ac761-0 kmKRMV">AI Regulation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-resilience" class="sc-e69ac761-0 kmKRMV">AI Resilience</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-bias" class="sc-e69ac761-0 kmKRMV">Machine Learning Bias</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-life-cycle-management" class="sc-e69ac761-0 kmKRMV">Machine Learning Life Cycle Management</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-translation" class="sc-e69ac761-0 kmKRMV">Machine Translation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mlops" class="sc-e69ac761-0 kmKRMV">MLOps</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/monte-carlo-learning" class="sc-e69ac761-0 kmKRMV">Monte Carlo Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multi-task-learning" class="sc-e69ac761-0 kmKRMV">Multi-task Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/naive-bayes-classifier" class="sc-e69ac761-0 kmKRMV">Naive Bayes Classifier</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-neuron" class="sc-e69ac761-0 kmKRMV">Machine Learning Neuron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pooling-machine-learning" class="sc-e69ac761-0 kmKRMV">Pooling (Machine Learning)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/principal-component-analysis" class="sc-e69ac761-0 kmKRMV">Principal Component Analysis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-preprocessing" class="sc-e69ac761-0 kmKRMV">Machine Learning Preprocessing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rectified-linear-unit-relu" class="sc-e69ac761-0 kmKRMV">Rectified Linear Unit (ReLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/reproducibility-machine-learning" class="sc-e69ac761-0 kmKRMV">Reproducibility in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/restricted-boltzmann-machines" class="sc-e69ac761-0 kmKRMV">Restricted Boltzmann Machines</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-supervised-learning" class="sc-e69ac761-0 kmKRMV">Semi-Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/supervised-learning" class="sc-e69ac761-0 kmKRMV">Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/support-vector-machines-svm" class="sc-e69ac761-0 kmKRMV">Support Vector Machines (SVM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/topic-modeling" class="sc-e69ac761-0 kmKRMV">Topic Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/uncertainty-machine-learning" class="sc-e69ac761-0 kmKRMV">Uncertainty in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/vanishing-and-exploding-gradients" class="sc-e69ac761-0 kmKRMV">Vanishing and Exploding Gradients</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-interpretability" class="sc-e69ac761-0 kmKRMV">AI Interpretability</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-labeling" class="sc-e69ac761-0 kmKRMV">Data Labeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/inference-engine" class="sc-e69ac761-0 kmKRMV">Inference Engine</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/probabilistic-models-in-machine-learning" class="sc-e69ac761-0 kmKRMV">Probabilistic Models in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f1-score-machine-learning" class="sc-e69ac761-0 kmKRMV">F1 Score in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/expectation-maximization" class="sc-e69ac761-0 kmKRMV">Expectation Maximization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/beam-search-algorithm" class="sc-e69ac761-0 kmKRMV">Beam Search Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/embedding-layer" class="sc-e69ac761-0 kmKRMV">Embedding Layer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/differential-privacy" class="sc-e69ac761-0 kmKRMV">Differential Privacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-poisoning" class="sc-e69ac761-0 kmKRMV">Data Poisoning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/causal-inference" class="sc-e69ac761-0 kmKRMV">Causal Inference</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/capsule-neural-network" class="sc-e69ac761-0 kmKRMV">Capsule Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/attention-mechanisms" class="sc-e69ac761-0 kmKRMV">Attention Mechanisms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/domain-adaptation" class="sc-e69ac761-0 kmKRMV">Domain Adaptation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/evolutionary-algorithms" class="sc-e69ac761-0 kmKRMV">Evolutionary Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/contrastive-learning" class="sc-e69ac761-0 kmKRMV">Contrastive Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/explainable-ai" class="sc-e69ac761-0 kmKRMV">Explainable AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/affective-ai" class="sc-e69ac761-0 kmKRMV">Affective AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-networks" class="sc-e69ac761-0 kmKRMV">Semantic Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-augmentation" class="sc-e69ac761-0 kmKRMV">Data Augmentation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/convolutional-neural-networks" class="sc-e69ac761-0 kmKRMV">Convolutional Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-computing" class="sc-e69ac761-0 kmKRMV">Cognitive Computing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/end-to-end-learning" class="sc-e69ac761-0 kmKRMV">End-to-end Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-tuning" class="sc-e69ac761-0 kmKRMV">Prompt Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/double-descent" class="sc-e69ac761-0 kmKRMV">Double Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/model-drift" class="sc-e69ac761-0 kmKRMV">Model Drift</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-radiance-fields" class="sc-e69ac761-0 kmKRMV">Neural Radiance Fields</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/regularization" class="sc-e69ac761-0 kmKRMV">Regularization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-querying" class="sc-e69ac761-0 kmKRMV">Natural Language Querying (NLQ)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/foundation-models" class="sc-e69ac761-0 kmKRMV">Foundation Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/forward-propagation" class="sc-e69ac761-0 kmKRMV">Forward Propagation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f2-score" class="sc-e69ac761-0 kmKRMV">F2 Score</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-ethics" class="sc-e69ac761-0 kmKRMV">AI Ethics</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/transfer-learning" class="sc-e69ac761-0 kmKRMV">Transfer Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-alignment" class="sc-e69ac761-0 kmKRMV">AI Alignment</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/whisper-v3" class="sc-e69ac761-0 kmKRMV">Whisper v3</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/whisper-v2" class="sc-e69ac761-0 kmKRMV">Whisper v2</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-structured-data" class="sc-e69ac761-0 kmKRMV">Semi-structured data</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-hallucinations" class="sc-e69ac761-0 kmKRMV">AI Hallucinations</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/emergent-behavior" class="sc-e69ac761-0 kmKRMV">Emergent Behavior</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/matplotlib" class="sc-e69ac761-0 kmKRMV">Matplotlib</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/numpy" class="sc-e69ac761-0 kmKRMV">NumPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scikit-learn" class="sc-e69ac761-0 kmKRMV">Scikit-learn</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scipy" class="sc-e69ac761-0 kmKRMV">SciPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keras" class="sc-e69ac761-0 kmKRMV">Keras</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensorflow" class="sc-e69ac761-0 kmKRMV">TensorFlow</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/seaborn-python-package" class="sc-e69ac761-0 kmKRMV">Seaborn Python Package</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pytorch" class="sc-e69ac761-0 kmKRMV">PyTorch</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-toolkit-nltk" class="sc-e69ac761-0 kmKRMV">Natural Language Toolkit (NLTK)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pandas" class="sc-e69ac761-0 kmKRMV">Pandas</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ego-4d" class="sc-e69ac761-0 kmKRMV">Ego 4D</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/the-pile" 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print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root MuiButton-root MuiButton-text MuiButton-textPrimary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorPrimary MuiButton-root MuiButton-text MuiButton-textPrimary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorPrimary sc-d74559df-0 iotBFW css-ak4akz" tabindex="0" type="button" href="" target="_self"><span class="sc-b323b31-0 btXZGI button-label" display="inline-flex">Z</span></button></li></ul></div><div class="sc-ace17a57-0 sc-8e7440ad-1 kpvNPn cxZbdr"><div data-letter="#" class="sc-ace17a57-0 hrNfIL"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 lnNnlT">AI Glossary Categories</span><ul display="none" class="sc-ace17a57-0 itDcJo"><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" fill="gray-200" stroke="gray-200"><use href="/svgs/icons-updated.svg#plus-icon=solid"></use></svg></span><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jBPWIy">Datasets</span></button><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" fill="gray-200" stroke="gray-200"><use href="/svgs/icons-updated.svg#plus-icon=solid"></use></svg></span><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jBPWIy">Fundamentals</span></button><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" fill="gray-200" stroke="gray-200"><use href="/svgs/icons-updated.svg#plus-icon=solid"></use></svg></span><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jBPWIy">Models</span></button><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" 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href="/ai-glossary/batch-gradient-descent" class="sc-e69ac761-0 dsCZKx">Batch Gradient Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/backpropagation" class="sc-e69ac761-0 dsCZKx">Backpropagation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bayesian-machine-learning" class="sc-e69ac761-0 dsCZKx">Bayesian Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bert" class="sc-e69ac761-0 dsCZKx">BERT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/binary-classification-ai" class="sc-e69ac761-0 dsCZKx">Binary Classification AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/beam-search-algorithm" class="sc-e69ac761-0 dsCZKx">Beam Search Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bias-variance-tradeoff" class="sc-e69ac761-0 dsCZKx">Bias-Variance Tradeoff</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/boosting" class="sc-e69ac761-0 dsCZKx">Boosting in Machine Learning</a></li><li data-letter="C" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">C</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/chess-bots" class="sc-e69ac761-0 dsCZKx">Chess bots</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/chatgpt" class="sc-e69ac761-0 dsCZKx">ChatGPT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cure-algorithm" class="sc-e69ac761-0 dsCZKx">CURE Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/curse-of-dimensionality" class="sc-e69ac761-0 dsCZKx">Curse of Dimensionality</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/classification" class="sc-e69ac761-0 dsCZKx">Classification</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-map" class="sc-e69ac761-0 dsCZKx">Cognitive Map</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/central-processing-unit-cpu" class="sc-e69ac761-0 dsCZKx">Central Processing Unit (CPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conditional-variational-autoencoders" class="sc-e69ac761-0 dsCZKx">Conditional Variational Autoencoders</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conversational-ai" class="sc-e69ac761-0 dsCZKx">Conversational AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/causal-inference" class="sc-e69ac761-0 dsCZKx">Causal Inference</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/capsule-neural-network" class="sc-e69ac761-0 dsCZKx">Capsule Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/contrastive-learning" class="sc-e69ac761-0 dsCZKx">Contrastive Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/convolutional-neural-networks" class="sc-e69ac761-0 dsCZKx">Convolutional Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-computing" class="sc-e69ac761-0 dsCZKx">Cognitive Computing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/common-crawl-datasets" class="sc-e69ac761-0 dsCZKx">Common Crawl Datasets</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/clustering-algorithms" class="sc-e69ac761-0 dsCZKx">Clustering Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/concatenative-synthesis" class="sc-e69ac761-0 dsCZKx">Concatenative Synthesis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/candidate-sampling" class="sc-e69ac761-0 dsCZKx">Candidate Sampling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-creativity" class="sc-e69ac761-0 dsCZKx">Computational Creativity</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/context-aware-computing" class="sc-e69ac761-0 dsCZKx">Context-Aware Computing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/counterfactual-explanations-in-ai" class="sc-e69ac761-0 dsCZKx">Counterfactual Explanations in AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-architectures" class="sc-e69ac761-0 dsCZKx">Cognitive Architectures</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-phenotyping" class="sc-e69ac761-0 dsCZKx">Computational Phenotyping</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/continuous-learning-systems" class="sc-e69ac761-0 dsCZKx">Continuous Learning Systems</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/chatbots" class="sc-e69ac761-0 dsCZKx">Chatbots</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/composite-ai" class="sc-e69ac761-0 dsCZKx">Composite AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-lingustics" class="sc-e69ac761-0 dsCZKx">Computational Linguistics</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-semantics" class="sc-e69ac761-0 dsCZKx">Computational Semantics</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/collaborative-filtering" class="sc-e69ac761-0 dsCZKx">Collaborative Filtering</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/corpus" class="sc-e69ac761-0 dsCZKx">Corpus in NLP</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/confirmation-bias" class="sc-e69ac761-0 dsCZKx">Confirmation Bias in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/confidence-intervals" class="sc-e69ac761-0 dsCZKx">Confidence Intervals in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cross-validation" class="sc-e69ac761-0 dsCZKx">Cross Validation in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/clustering" class="sc-e69ac761-0 dsCZKx">Clustering in Machine Learning</a></li><li data-letter="D" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">D</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/dall-e" class="sc-e69ac761-0 dsCZKx">Dall-E</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/distilbert" class="sc-e69ac761-0 dsCZKx">DistilBERT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/dimensionality-reduction" class="sc-e69ac761-0 dsCZKx">Dimensionality Reduction</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deep-reinforcement-learning" class="sc-e69ac761-0 dsCZKx">Deep Reinforcement Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/diffusion-model" class="sc-e69ac761-0 dsCZKx">Diffusion Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deep-learning" class="sc-e69ac761-0 dsCZKx">Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/datasets" class="sc-e69ac761-0 dsCZKx">Datasets</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-labeling" class="sc-e69ac761-0 dsCZKx">Data Labeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/differential-privacy" class="sc-e69ac761-0 dsCZKx">Differential Privacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-poisoning" class="sc-e69ac761-0 dsCZKx">Data Poisoning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/domain-adaptation" class="sc-e69ac761-0 dsCZKx">Domain Adaptation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-augmentation" class="sc-e69ac761-0 dsCZKx">Data Augmentation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/double-descent" class="sc-e69ac761-0 dsCZKx">Double Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deepfake-detection" class="sc-e69ac761-0 dsCZKx">Deepfake Detection</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/diffusion" class="sc-e69ac761-0 dsCZKx">Diffusion</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-drift" class="sc-e69ac761-0 dsCZKx">Data Drift</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-scarcity" class="sc-e69ac761-0 dsCZKx">Data Scarcity</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/decision-tree" class="sc-e69ac761-0 dsCZKx">Decision Tree</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/decision-intelligence" class="sc-e69ac761-0 dsCZKx">Decision Intelligence</a></li><li data-letter="E" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">E</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/expectation-maximization" class="sc-e69ac761-0 dsCZKx">Expectation Maximization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/embedding-layer" class="sc-e69ac761-0 dsCZKx">Embedding Layer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/evolutionary-algorithms" class="sc-e69ac761-0 dsCZKx">Evolutionary Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/explainable-ai" class="sc-e69ac761-0 dsCZKx">Explainable AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/end-to-end-learning" class="sc-e69ac761-0 dsCZKx">End-to-end Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/emergent-behavior" class="sc-e69ac761-0 dsCZKx">Emergent Behavior</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ego-4d" class="sc-e69ac761-0 dsCZKx">Ego 4D</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/eco-friendly-ai" class="sc-e69ac761-0 dsCZKx">Eco-friendly AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ensemble-learning" class="sc-e69ac761-0 dsCZKx">Ensemble Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/entropy" class="sc-e69ac761-0 dsCZKx">Entropy in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/epoch" class="sc-e69ac761-0 dsCZKx">Epoch in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ethical-ai" class="sc-e69ac761-0 dsCZKx">Ethical AI</a></li><li data-letter="F" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">F</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/flajolet-martin-algorithm" class="sc-e69ac761-0 dsCZKx">Flajolet-Martin Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feedforward-neural-network" class="sc-e69ac761-0 dsCZKx">Feedforward Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fine-tuning-deep-learning" class="sc-e69ac761-0 dsCZKx">Fine Tuning in Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fundamentals" class="sc-e69ac761-0 dsCZKx">Fundamentals</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f1-score-machine-learning" class="sc-e69ac761-0 dsCZKx">F1 Score in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/foundation-models" class="sc-e69ac761-0 dsCZKx">Foundation Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/forward-propagation" class="sc-e69ac761-0 dsCZKx">Forward Propagation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f2-score" class="sc-e69ac761-0 dsCZKx">F2 Score</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feature-store-for-machine-learning" class="sc-e69ac761-0 dsCZKx">Feature Store for Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/few-shot-learning" class="sc-e69ac761-0 dsCZKx">Few Shot Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/federated-learning" class="sc-e69ac761-0 dsCZKx">Federated Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feature-learning" class="sc-e69ac761-0 dsCZKx">Feature Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feature-selection" class="sc-e69ac761-0 dsCZKx">Feature Selection</a></li><li data-letter="G" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">G</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-clipping" class="sc-e69ac761-0 dsCZKx">Gradient Clipping</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-adversarial-networks" class="sc-e69ac761-0 dsCZKx">Generative Adversarial Networks (GANs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gaussian-processes" class="sc-e69ac761-0 dsCZKx">Gaussian Processes</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gated-recurrent-unit" class="sc-e69ac761-0 dsCZKx">Gated Recurrent Unit</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/grounding" class="sc-e69ac761-0 dsCZKx">Grounding</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-boosting-machines" class="sc-e69ac761-0 dsCZKx">Gradient Boosting Machines (GBMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/graphics-processing-unit-gpu" class="sc-e69ac761-0 dsCZKx">Graphics Processing Unit (GPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/google-bard" class="sc-e69ac761-0 dsCZKx">Google's Bard</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-scaling" class="sc-e69ac761-0 dsCZKx">Gradient Scaling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-ai" class="sc-e69ac761-0 dsCZKx">Generative AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/graph-neural-networks" class="sc-e69ac761-0 dsCZKx">Graph Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/grapheme-to-phoneme-conversion-g2p" class="sc-e69ac761-0 dsCZKx">Grapheme-to-Phoneme Conversion (G2P)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-teaching-networks" class="sc-e69ac761-0 dsCZKx">Generative Teaching Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/genetic-algorithms" class="sc-e69ac761-0 dsCZKx">Genetic Algorithms in AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ground-truth" class="sc-e69ac761-0 dsCZKx">Ground Truth in Machine Learning</a></li><li data-letter="H" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">H</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hyperparameters" class="sc-e69ac761-0 dsCZKx">Hyperparameters</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hidden-markov-models" class="sc-e69ac761-0 dsCZKx">Hidden Markov Models (HMMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hooke-jeeves-algorithm" class="sc-e69ac761-0 dsCZKx">Hooke-Jeeves Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hyperparameter-tuning" class="sc-e69ac761-0 dsCZKx">Hyperparameter Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/homograph-disambiguation" class="sc-e69ac761-0 dsCZKx">Homograph Disambiguation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/human-centered-ai" class="sc-e69ac761-0 dsCZKx">Human-centered AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hidden-layer" class="sc-e69ac761-0 dsCZKx">Hidden Layer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/human-in-the-loop-ai" class="sc-e69ac761-0 dsCZKx">Human-in-the-Loop AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hybrid-ai" class="sc-e69ac761-0 dsCZKx">Hybrid AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/human-augmentation-with-ai" class="sc-e69ac761-0 dsCZKx">Human Augmentation with AI</a></li><li data-letter="I" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">I</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/inference-engine" class="sc-e69ac761-0 dsCZKx">Inference Engine</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/intelligent-document-processing" class="sc-e69ac761-0 dsCZKx">Intelligent Document Processing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/imbalanced-data" class="sc-e69ac761-0 dsCZKx">Imbalanced Data</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/instruction-tuning" class="sc-e69ac761-0 dsCZKx">Instruction Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/incremental-learning" class="sc-e69ac761-0 dsCZKx">Incremental Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/information-retrieval" class="sc-e69ac761-0 dsCZKx">Information Retrieval</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/image-recognition" class="sc-e69ac761-0 dsCZKx">Image Recognition</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/imagenet" class="sc-e69ac761-0 dsCZKx">ImageNet</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/inductive-bias" class="sc-e69ac761-0 dsCZKx">Inductive Bias</a></li><li data-letter="K" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 fvIpti">K</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/k-shingles" class="sc-e69ac761-0 dsCZKx">k-Shingles</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keyphrase-extraction" class="sc-e69ac761-0 ezFGlW">Keyphrase Extraction</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keras" class="sc-e69ac761-0 dsCZKx">Keras</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/knowledge-distillation" class="sc-e69ac761-0 dsCZKx">Knowledge Distillation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/knowledge-representation-and-reasoning" class="sc-e69ac761-0 dsCZKx">Knowledge Representation and Reasoning</a></li><li data-letter="L" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">L</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/llama-2" class="sc-e69ac761-0 dsCZKx">Llama 2</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/llm-collection" class="sc-e69ac761-0 dsCZKx">LLM Collection</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/latent-dirichlet-allocation" class="sc-e69ac761-0 dsCZKx">Latent Dirichlet Allocation (LDA)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/large-language-model" class="sc-e69ac761-0 dsCZKx">Large Language Model (LLM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/loss-function" class="sc-e69ac761-0 dsCZKx">Loss Function</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/limited-memory-ai" class="sc-e69ac761-0 dsCZKx">Limited Memory AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/learning-rate" class="sc-e69ac761-0 dsCZKx">Learning Rate</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/learning-to-rank" class="sc-e69ac761-0 dsCZKx">Learning To Rank</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/logits" class="sc-e69ac761-0 dsCZKx">Logits</a></li><li data-letter="M" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">M</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/midjourney-image-generation" class="sc-e69ac761-0 dsCZKx">Midjourney (Image Generation)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mistral" class="sc-e69ac761-0 dsCZKx">Mistral</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mixture-of-experts" class="sc-e69ac761-0 dsCZKx">Mixture of Experts</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-ai" class="sc-e69ac761-0 dsCZKx">Multimodal AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-learning" class="sc-e69ac761-0 dsCZKx">Multimodal Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning" class="sc-e69ac761-0 dsCZKx">Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-aI-models-and-modalities" class="sc-e69ac761-0 dsCZKx">Multimodal AI Models and Modalities</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mamba" class="sc-e69ac761-0 dsCZKx">Mamba</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/models" class="sc-e69ac761-0 dsCZKx">Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-bias" class="sc-e69ac761-0 dsCZKx">Machine Learning Bias</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-life-cycle-management" class="sc-e69ac761-0 dsCZKx">Machine Learning Life Cycle Management</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-translation" class="sc-e69ac761-0 dsCZKx">Machine Translation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mlops" class="sc-e69ac761-0 dsCZKx">MLOps</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/monte-carlo-learning" class="sc-e69ac761-0 dsCZKx">Monte Carlo Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multi-task-learning" class="sc-e69ac761-0 dsCZKx">Multi-task Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-neuron" class="sc-e69ac761-0 dsCZKx">Machine Learning Neuron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-preprocessing" class="sc-e69ac761-0 dsCZKx">Machine Learning Preprocessing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/model-drift" class="sc-e69ac761-0 dsCZKx">Model Drift</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/matplotlib" class="sc-e69ac761-0 dsCZKx">Matplotlib</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/Markov-decision-process" class="sc-e69ac761-0 dsCZKx">Markov Decision Process</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/model-interpretability" class="sc-e69ac761-0 dsCZKx">Model Interpretability</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-algorithmic-trading" class="sc-e69ac761-0 dsCZKx">Machine Learning in Algorithmic Trading</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/metacognitive-learning-models" class="sc-e69ac761-0 dsCZKx">Metacognitive Learning Models </a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/metaheuristic-algorithms" class="sc-e69ac761-0 dsCZKx">Metaheuristic Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multitask-prompt-tuning" class="sc-e69ac761-0 dsCZKx">Multitask Prompt Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multi-agent-systems" class="sc-e69ac761-0 dsCZKx">Multi-Agent Systems</a></li><li data-letter="N" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">N</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-processing" class="sc-e69ac761-0 dsCZKx">Natural Language Processing (NLP)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-generation" class="sc-e69ac761-0 dsCZKx">Natural Language Generation (NLG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-understanding" class="sc-e69ac761-0 dsCZKx">Natural Language Understanding (NLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neuralink" class="sc-e69ac761-0 dsCZKx">Neuralink</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/naive-bayes-classifier" class="sc-e69ac761-0 dsCZKx">Naive Bayes Classifier</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-radiance-fields" class="sc-e69ac761-0 dsCZKx">Neural Radiance Fields</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-querying" class="sc-e69ac761-0 dsCZKx">Natural Language Querying (NLQ)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/numpy" class="sc-e69ac761-0 dsCZKx">NumPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-toolkit-nltk" class="sc-e69ac761-0 dsCZKx">Natural Language Toolkit (NLTK)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-architecture-search" class="sc-e69ac761-0 dsCZKx">Neural Architecture Search</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-text-to-speech-ntts" class="sc-e69ac761-0 dsCZKx">Neural Text-to-Speech (NTTS)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/named-entity-recognition" class="sc-e69ac761-0 dsCZKx">Named Entity Recognition</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-style-transfer" class="sc-e69ac761-0 dsCZKx">Neural Style Transfer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neuroevolution" class="sc-e69ac761-0 dsCZKx">Neuroevolution</a></li><li data-letter="O" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">O</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/online-gradient-descent" class="sc-e69ac761-0 dsCZKx">Online Gradient Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/overfitting-underfitting" class="sc-e69ac761-0 dsCZKx">Overfitting and Underfitting</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-whisper" class="sc-e69ac761-0 dsCZKx">OpenAI Whisper</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-sora" class="sc-e69ac761-0 dsCZKx">OpenAI Sora</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/out-of-distribution-detection" class="sc-e69ac761-0 dsCZKx">Out-of-Distribution Detection</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/one-shot-learning" class="sc-e69ac761-0 dsCZKx">One-Shot Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/objective-function" class="sc-e69ac761-0 dsCZKx">Objective Function</a></li><li data-letter="P" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">P</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-engineering" class="sc-e69ac761-0 dsCZKx">Prompt Engineering</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-chaining" class="sc-e69ac761-0 dsCZKx">Prompt Chaining</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/perceptron" class="sc-e69ac761-0 dsCZKx">Perceptron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/precision-and-recall" class="sc-e69ac761-0 dsCZKx">Precision and Recall</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/packages-glossary" class="sc-e69ac761-0 dsCZKx">Packages</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pooling-machine-learning" class="sc-e69ac761-0 dsCZKx">Pooling (Machine Learning)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/principal-component-analysis" class="sc-e69ac761-0 dsCZKx">Principal Component Analysis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/probabilistic-models-in-machine-learning" class="sc-e69ac761-0 dsCZKx">Probabilistic Models in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-tuning" class="sc-e69ac761-0 dsCZKx">Prompt Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pytorch" class="sc-e69ac761-0 dsCZKx">PyTorch</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pandas" class="sc-e69ac761-0 dsCZKx">Pandas</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/parametric-neural-networks%C2%A0" class="sc-e69ac761-0 dsCZKx">Parametric Neural Networks </a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pretraining" class="sc-e69ac761-0 dsCZKx">Pretraining</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/part-of-speech-tagging" class="sc-e69ac761-0 dsCZKx">Part-of-Speech Tagging</a></li><li data-letter="Q" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">Q</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/quantum-machine-learning-algorithms" class="sc-e69ac761-0 dsCZKx">Quantum Machine Learning Algorithms</a></li><li data-letter="R" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">R</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rule-based-ai" class="sc-e69ac761-0 dsCZKx">Rule-Based AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/roberta" class="sc-e69ac761-0 dsCZKx">RoBERTa</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rlhf" class="sc-e69ac761-0 dsCZKx">RLHF</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/retrieval-augmented-generation" class="sc-e69ac761-0 dsCZKx">Retrieval-Augmented Generation (RAG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/representation-learning" class="sc-e69ac761-0 dsCZKx">Representation Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rectified-linear-unit-relu" class="sc-e69ac761-0 dsCZKx">Rectified Linear Unit (ReLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/reproducibility-machine-learning" class="sc-e69ac761-0 dsCZKx">Reproducibility in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/restricted-boltzmann-machines" class="sc-e69ac761-0 dsCZKx">Restricted Boltzmann Machines</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/regularization" class="sc-e69ac761-0 dsCZKx">Regularization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/recurrent-neural-networks" class="sc-e69ac761-0 dsCZKx">Recurrent Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/random-forest" class="sc-e69ac761-0 dsCZKx">Random Forest</a></li><li data-letter="S" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">S</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/sentiment-analysis" class="sc-e69ac761-0 dsCZKx">Sentiment Analysis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/speech-to-text-models" class="sc-e69ac761-0 dsCZKx">Speech-to-text models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/sequence-modeling" class="sc-e69ac761-0 dsCZKx">Sequence Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-kernel" class="sc-e69ac761-0 dsCZKx">Semantic Kernel</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-supervised-learning" class="sc-e69ac761-0 dsCZKx">Semi-Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/supervised-learning" class="sc-e69ac761-0 dsCZKx">Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/support-vector-machines-svm" class="sc-e69ac761-0 dsCZKx">Support Vector Machines (SVM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-networks" class="sc-e69ac761-0 dsCZKx">Semantic Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-structured-data" class="sc-e69ac761-0 dsCZKx">Semi-structured data</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scikit-learn" class="sc-e69ac761-0 dsCZKx">Scikit-learn</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scipy" class="sc-e69ac761-0 dsCZKx">SciPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/seaborn-python-package" class="sc-e69ac761-0 dsCZKx">Seaborn Python Package</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/squad" class="sc-e69ac761-0 dsCZKx">SQuAD</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/synthetic-data-for-ai-training" class="sc-e69ac761-0 dsCZKx">Synthetic Data for AI Training</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/statistical-relational-learning" class="sc-e69ac761-0 dsCZKx">Statistical Relational Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/self-healing-ai" class="sc-e69ac761-0 dsCZKx">Self-healing AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-search-algorithms" class="sc-e69ac761-0 dsCZKx">Semantic Search Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/symbolic-ai" class="sc-e69ac761-0 dsCZKx">Symbolic AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/spike-neural-networks" class="sc-e69ac761-0 dsCZKx">Spike Neural Networks</a></li><li data-letter="T" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">T</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/text-to-speech-models" class="sc-e69ac761-0 dsCZKx">Text-to-Speech Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/transformers" class="sc-e69ac761-0 dsCZKx">Transformers</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensor-processing-unit-tpu" class="sc-e69ac761-0 dsCZKx">Tensor Processing Unit (TPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tokenization" class="sc-e69ac761-0 dsCZKx">Tokenization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/techniques" class="sc-e69ac761-0 dsCZKx">Techniques</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/topic-modeling" class="sc-e69ac761-0 dsCZKx">Topic Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/transfer-learning" class="sc-e69ac761-0 dsCZKx">Transfer Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensorflow" class="sc-e69ac761-0 dsCZKx">TensorFlow</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/the-pile" class="sc-e69ac761-0 dsCZKx">The Pile</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/test-data-set" class="sc-e69ac761-0 dsCZKx">Test Data Set</a></li><li data-letter="U" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">U</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/uncertainty-machine-learning" class="sc-e69ac761-0 dsCZKx">Uncertainty in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/unsupervised-learning" class="sc-e69ac761-0 dsCZKx">Unsupervised Learning</a></li><li data-letter="V" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">V</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/vanishing-and-exploding-gradients" class="sc-e69ac761-0 dsCZKx">Vanishing and Exploding Gradients</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/voice-cloning" class="sc-e69ac761-0 dsCZKx">Voice Cloning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/validation-data-set" class="sc-e69ac761-0 dsCZKx">Validation Data Set</a></li><li data-letter="W" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">W</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/winnow-algorithm" class="sc-e69ac761-0 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Deepgram</span></div></div></div></footer></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"slug":["keyphrase-extraction"],"pageData":{"templateGlossary":{"__typename":"TemplateGlossaryRecord","id":"BLfo9azwSZiRgZRLBIaPbQ","seo":null,"slug":"keyphrase-extraction","title":"Keyphrase Extraction","categoryPage":false,"excerpt":{"__typename":"TemplateGlossaryModelExcerptField","value":{"schema":"dast","document":{"type":"root","children":[{"type":"paragraph","children":[{"type":"span","value":"Keyphrase extraction is crucial for analyzing customer reviews, understanding sentiments, and spotting emerging trends. 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To learn how engineers can cheat and how to spot it, check out [this article](https://deepgram.com/learn/lies-damn-lies-and-benchmarks).","imageDesktop":{"__typename":"FileField","id":"YPlSfhX7Q2en_IPBQQNdIA","title":null,"filename":"screen-shot-2024-06-16-at-2-18-35-am.png","size":2642655,"alt":null,"url":"https://www.datocms-assets.com/96965/1718529518-screen-shot-2024-06-16-at-2-18-35-am.png","width":1504,"height":972},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"OcUPVLYOTdiOH90vXDPFbA","caption":"Mixture of Experts (MoE) is a method that presents an efficient approach to dramatically increasing a model’s capabilities without introducing a proportional amount of computational overhead. To learn more, check out [this guide](https://deepgram.com/learn/mixture-of-experts-ml-model-guide)!","imageDesktop":{"__typename":"FileField","id":"cnmz5FWiQv2S--QFhqNL-g","title":null,"filename":"screen-shot-2024-06-16-at-2-06-22-am.png","size":2233720,"alt":null,"url":"https://www.datocms-assets.com/96965/1718528786-screen-shot-2024-06-16-at-2-06-22-am.png","width":1512,"height":788},"imageMobile":null,"link":""}],"value":{"schema":"dast","document":{"type":"root","children":[{"type":"paragraph","children":[{"type":"span","value":"Keyphrase extraction in "},{"url":"https://deepgram.com/ai-glossary/natural-language-processing","type":"link","children":[{"type":"span","marks":["underline"],"value":"natural language processing (NLP)"}]},{"type":"span","value":" involves pulling out the most important phrases from a document and capturing their essence. This technique facilitates document similarity checks and enhances search efficiency. Matching user queries with extracted phrases speeds up retrieval, especially in large databases."}]},{"type":"paragraph","children":[{"type":"span","value":"In business, keyphrase extraction is crucial for analyzing customer reviews, understanding sentiments, and spotting emerging trends. It's difficult, demanding attention to linguistic details, context, and document structure. Yet, it plays a vital role in information retrieval and NLP."}]},{"type":"heading","level":2,"children":[{"type":"span","value":"Understanding Keyphrase Extraction"}]},{"type":"paragraph","children":[{"type":"span","value":"The keyphrase extraction process is divided into two stages: "}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Extraction of Candidate Phrases"},{"type":"span","value":": It involves studying how words are used and the way documents are written, looking for possible phrases based on specific rules, like how often they appear or how important they are, and then using either manual or automated methods, such as "},{"url":"https://en.wikipedia.org/wiki/Regular_expression","type":"link","children":[{"type":"span","marks":["underline"],"value":"regular expressions"}]},{"type":"span","value":", to pull out the important phrases. These hard-coded extracted phrases are termed "},{"type":"span","marks":["strong"],"value":"candidate phrases"},{"type":"span","value":"."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong"],"value":"Ranking of Candidate Phrases: "},{"type":"span","value":"Once candidate phrases have been identified, the ranking is determined based on their relevance to a text document of interest. The candidate phrases that rank the highest are keyphrases for that document. This ranking process is typically accomplished using specialized algorithms like….. "}]}]}]},{"type":"paragraph","children":[{"type":"span","value":"First, the candidate phrases are converted into numerical vectors in a process called"},{"url":"https://en.wikipedia.org/wiki/Word_embedding","type":"link","children":[{"type":"span","marks":["underline"],"value":" word embedding"}]},{"type":"span","value":". After that, we take the entire document, let's say an article on artificial intelligence, and represent it as another vector. Finally, we compare the vectors of the candidate phrases with the vector of the entire document. This comparison helps us understand how closely these phrases relate to the content of the article, allowing us to assess their similarity and rank their importance within the context of the document."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Keyphrase Extraction compared to other NLP Techniques"}]},{"type":"paragraph","children":[{"type":"span","value":"Keyphrase extraction shares similarities with other NLP techniques. In this section, we will break down the similarities and differences."}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Keyword Extraction: "},{"type":"span","value":"Keyword and keyphrase extraction are often confused but have distinct goals. Keyword extraction aims to extract important words from a document. In contrast, keyphrase extraction targets grouped words that form phrases. Think of keyword extraction as a part of keyphrase extraction—they use similar techniques."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong"],"value":"Text Summarization: "},{"type":"span","value":"Text summarization is an NLP technique where a lengthy document is condensed while keeping its meaning intact. Keyphrase extraction helps in summarization by ensuring essential phrases are included, irrespective of the document's size"}]}]}]},{"type":"paragraph","children":[{"type":"span","value":"Both techniques differ in their usage and implementation. Summaries are often generated by extracting key sentences from a document. This technique, known as "},{"url":"https://blog.paperspace.com/extractive-and-abstractive-summarization-techniques/#extractive-summarization","type":"link","children":[{"type":"span","marks":["underline"],"value":"extractive text summarization"}]},{"type":"span","value":", is similar to those used for extracting phrases. However, more advanced NLP techniques are now used to generate summaries in practice."}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong"],"value":"Information Extraction: "},{"type":"span","value":"Information extraction involves retrieving organized information like dates, location or any relevant information in a text document. In contrast, keyphrase extraction is used to identify key terms or phrases that represent the themes of a document. Since text is unstructured, we use information extraction to pull out useful details in a structured way. Techniques like Named Entity Recognition ("},{"url":"https://deepgram.com/ai-glossary/natural-language-processing#named-entity-recognition","type":"link","children":[{"type":"span","marks":["underline"],"value":"NER"}]},{"type":"span","value":") are valuable for information extraction tasks."}]}]}]},{"type":"heading","level":2,"children":[{"type":"span","value":"Techniques for Keyphrase Extraction"}]},{"type":"paragraph","children":[{"type":"span","value":"Keyphrase Extraction Techniques can be categorized into two groups:"}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","value":"Supervised techniques."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","value":"Unsupervised techniques."}]}]}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Supervised Keyphrase Extraction Techniques"}]},{"type":"paragraph","children":[{"type":"span","value":"In this method, you train a model with a dataset of labeled keyphrases for a particular domain. For instance, we can have a dataset of keyphrases for a specific domain, like business. When extracting keyphrases from a new document, the model decides whether each candidate phrase is a keyphrase."}]},{"type":"paragraph","children":[{"type":"span","value":"While supervised techniques excel in their domain, creating the training dataset is time-consuming. They may perform poorly in different subjects due to specific training data characteristics."}]},{"type":"paragraph","children":[{"type":"span","value":"In supervised keyphrase extraction, tasks can be either classification (deciding if a phrase is a keyphrase) or ranking (assigning ranks to phrases). The "},{"url":"https://en.wikipedia.org/wiki/Ranking_SVM","type":"link","children":[{"type":"span","marks":["underline"],"value":"Ranking SVM"}]},{"type":"span","value":", using a support vector machine, is an example of a model for ranking tasks."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Unsupervised Keyphrase Extraction Techniques"}]},{"type":"paragraph","children":[{"type":"span","value":"Unsupervised keyphrase extraction techniques do not rely on a pre-existing dataset to train a model for extraction. Instead, they use methods ranging from analyzing a text's linguistic properties to utilizing language models for extraction."}]},{"type":"heading","level":4,"children":[{"type":"span","value":"Frequency-based method: TF-IDF"}]},{"type":"paragraph","children":[{"type":"span","value":"This simple and effective technique extracts phrases by focusing on their frequency in a document. It identifies commonly occurring word groups, assuming that important phrases will be repeated several times in the document."}]},{"type":"paragraph","children":[{"type":"span","value":"Term Frequency ("},{"url":"https://en.wikipedia.org/wiki/Tf%E2%80%93idf#:~:text=%EF%BF%BD-,Term%20frequency,-%5Bedit%5D","type":"link","children":[{"type":"span","marks":["underline"],"value":"TF"}]},{"type":"span","value":") is one such approach that is popularly used, especially in keyword extraction. It involves extracting the most frequently occurring word in the document. However, it can also be employed in keyphrase extraction by considering "},{"url":"https://en.wikipedia.org/wiki/N-gram","type":"link","children":[{"type":"span","marks":["underline"],"value":"n-grams"}]},{"type":"span","value":" greater than 1."}]},{"type":"paragraph","children":[{"type":"span","value":"Inverse Document Frequency ("},{"url":"https://en.wikipedia.org/wiki/Tf%E2%80%93idf#:~:text=%7D-,Inverse%20document%20frequency,-%5Bedit%5D","type":"link","children":[{"type":"span","marks":["underline"],"value":"IDF"}]},{"type":"span","value":") complements TF by assessing term rarity across documents. It can also analyze multiple paragraphs in one document. The phrases with the highest IDF in the document are considered keyphrases."}]},{"type":"paragraph","children":[{"type":"span","value":"While frequency-based approaches work in some cases, they have limitations. They might catch frequently occurring word sequences, but not all repetitions form meaningful phrases. "}]},{"type":"paragraph","children":[{"type":"span","value":"Frequency-based approaches can be effective in some contexts, but they have limitations that make them less reliable in certain situations. For example, while these approaches may identify frequently occurring word sequences, not all repetitions are meaningful phrases."}]},{"type":"heading","level":4,"children":[{"type":"span","value":"Linguistics method: Part-of-speech (POS) tagging"}]},{"type":"paragraph","children":[{"type":"span","value":"This technique involves breaking down the text into words and labeling each word with its "},{"url":"https://en.wikipedia.org/wiki/Part_of_speech#:~:text=In%20grammar%2C%20a%20part%20of,that%20have%20similar%20grammatical%20properties.","type":"link","children":[{"type":"span","marks":["underline"],"value":"part of speech (POS)"}]},{"type":"span","value":". Then, using predefined rules and patterns (regular expression), phrases are extracted based on linguistic properties like nouns, verbs, adjectives, etc., using regular expressions. "}]},{"type":"paragraph","children":[{"type":"span","value":"For instance, we can use a pattern to detect phrases without a subject and predicate. Regular expressions are also handy for spotting nouns, adjectives, and verb phrases."}]},{"type":"paragraph","children":[{"url":"https://en.wikipedia.org/wiki/Part-of-speech_tagging","type":"link","children":[{"type":"span","marks":["underline"],"value":"POS tagging"}]},{"type":"span","value":" is often the first step to finding potential phrases in a document. These phrases then go through a more advanced process to identify the actual keyphrases."}]},{"type":"heading","level":4,"children":[{"type":"span","value":"Vector Embeddings: Word2Vec, Doc2Vec and Glove"}]},{"type":"paragraph","children":[{"type":"span","value":"Embedding is another technique employed for keyphrase extraction. This process involves converting potential keyphrases into vector representations and then comparing them with the vector representation of the document. Using vector embedding for NLP was initially introduced with the "},{"url":"https://arxiv.org/abs/1301.3781","type":"link","children":[{"type":"span","marks":["underline"],"value":"Word2Vec"}]},{"type":"span","value":" algorithm, which proposed that words with similar meanings have similar vector representations. "}]},{"type":"paragraph","children":[{"type":"span","value":"Later developments, such as "},{"url":"https://medium.com/wisio/a-gentle-introduction-to-doc2vec-db3e8c0cce5e","type":"link","children":[{"type":"span","marks":["underline"],"value":"Doc2Vec"}]},{"type":"span","value":", extended this concept to entire documents. GloVe ("},{"url":"https://en.wikipedia.org/wiki/GloVe","type":"link","children":[{"type":"span","marks":["underline"],"value":"Glove Vectors"}]},{"type":"span","value":"), on the other hand, applies the same principles as Word2Vec but with a simple difference. GloVe uses global "},{"url":"https://en.wikipedia.org/wiki/Co-occurrence","type":"link","children":[{"type":"span","marks":["underline"],"value":"co-occurrence statistics"}]},{"type":"span","value":" across the entire corpus, while Word2Vec focuses on local context."}]},{"type":"heading","level":2,"children":[{"type":"span","value":"Evaluation Metrics for Keyphrase Extraction"}]},{"type":"paragraph","children":[{"type":"span","value":"When evaluating keyphrase extraction algorithms, one common approach is treating the task as a binary classification problem, where the algorithm predicts whether a candidate phrase is a keyphrase. But not all keyphrases are equally relevant to the text document. Some keyphrases may have a stronger relationship with the document content than others. Therefore, it's essential to use evaluation metrics that consider the ranking of keyphrases."}]},{"type":"paragraph","children":[{"type":"span","value":"This leads us to our two categories of metrics for Keyphrase Extraction: "}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","value":"Traditional Metrics "}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","value":"Rank-based Metrics."}]}]}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Traditional Metrics"}]},{"type":"paragraph","children":[{"type":"span","value":"These metrics are commonly employed in classification tasks. They include precision, recall, and the F1 score. Here's how you use them for keyphrase extraction:"}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong"],"value":"Precision: "},{"url":"https://deepgram.com/ai-glossary/precision-and-recall","type":"link","children":[{"type":"span","marks":["underline"],"value":"Precision"}]},{"type":"span","value":" for keyphrase extraction is the proportion of correctly identified keyphrases extracted by the algorithm. In other words, it measures the accuracy of the extracted keyphrases."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Recall:"},{"type":"span","marks":["strong"],"value":" "},{"url":"https://deepgram.com/ai-glossary/precision-and-recall","type":"link","children":[{"type":"span","marks":["underline"],"value":"Recall"}]},{"type":"span","value":" is the proportion of correctly identified keyphrases among all the relevant keyphrases in the document or corpus. It measures the completeness of the keyphrase extraction process."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"F1 Score:"},{"type":"span","value":" The "},{"url":"https://deepgram.com/ai-glossary/f1-score-machine-learning","type":"link","children":[{"type":"span","marks":["underline"],"value":"F1 score"}]},{"type":"span","value":" combines precision and recall into one metric, using the harmonic mean. In keyphrase extraction, we're not evaluating the model across all possible categories but focusing on its accuracy in extracting a set number of keyphrases from a document."}]}]}]},{"type":"paragraph","children":[{"type":"span","value":"These evaluation metrics are similar to top-k classification tasks, where the model is assessed on predicting the top-k classes with the highest confidence scores. For keyphrase extraction, metrics like "},{"type":"span","marks":["strong"],"value":"precision@k"},{"type":"span","value":", "},{"type":"span","marks":["strong"],"value":"recall@k"},{"type":"span","value":", and "},{"type":"span","marks":["strong"],"value":"F1 score@k"},{"type":"span","value":" specifically evaluate the model's effectiveness in identifying the most relevant keyphrases within a given limit, resembling top-k classification scenarios."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Rank-Based Metric"}]},{"type":"paragraph","children":[{"type":"span","value":"These metrics treat the keyphrase extraction task as a ranking problem, evaluating each extracted keyphrase based on its relevance to the document content."}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong"],"value":"Mean Reciprocal Rank (MRR): "},{"type":"span","value":"Mean Reciprocal Rank ("},{"url":"https://en.wikipedia.org/wiki/Mean_reciprocal_rank","type":"link","children":[{"type":"span","marks":["underline"],"value":"MRR"}]},{"type":"span","value":") is used to evaluate the effectiveness of ranking the extracted keyphrases. It measures the average quality of the ranking by assessing the reciprocal of the rank of the first correctly extracted keyphrase in the list of candidate keyphrases. In other words, MRR quantifies how quickly the algorithm can identify relevant keyphrases, with a higher MRR value indicating better performance. "}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong"],"value":"Mean Average Precision (MAP):"},{"type":"span","value":" Mean Average Precision ("},{"url":"https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision:~:text=precision.%5B15%5D-,Mean%20average%20precision,-%5Bedit%5D","type":"link","children":[{"type":"span","marks":["underline"],"value":"MAP"}]},{"type":"span","value":") is used to evaluate the overall ranking quality produced by the extraction algorithm across multiple documents. It calculates the average precision for each document and then computes the mean of these average precisions."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Normalized Discounted Cumulative Gain (nDCG): "},{"type":"span","value":"Normalized Discounted Cumulative Gain ("},{"url":"https://en.wikipedia.org/wiki/Discounted_cumulative_gain","type":"link","children":[{"type":"span","marks":["underline"],"value":"nDCG"}]},{"type":"span","value":") assesses how well the extracted keyphrases are ranked, considering their relevance and position in the list. It calculates the total gain by adding the relevance scores of keyphrases, and adjusting for their position. The ideal score then normalizes the gain to get the nDCG."}]}]}]},{"type":"paragraph","children":[{"item":"ZHaO8fOcRLmrH9udSjy3QQ","type":"inlineItem"}]},{"type":"heading","level":2,"children":[{"type":"span","value":"Challenges in Keyphrase Extraction"}]},{"type":"paragraph","children":[{"type":"span","value":"Keyphrase extraction encounters several challenges that can impede the performance of even state-of-the-art algorithms. Here are a few of these challenges:"}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Loss of Context:"},{"type":"span","marks":["strong"],"value":" "},{"type":"span","value":"Context is often lost once a keyphrase is extracted from a document. It becomes challenging to discern the relevance of a phrase when it is isolated from other words that provide additional context. As a result, the extracted phrase may be erroneously ranked lower than its actual relevance."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Ambiguity due to Polysemy:"},{"type":"span","value":" "},{"url":"https://en.wikipedia.org/wiki/Polysemy","type":"link","children":[{"type":"span","marks":["underline"],"value":"Polysemy"}]},{"type":"span","value":" is when a phrase has multiple meanings. Ambiguity arises due to this. A keyphrase algorithm must navigate through all of this in order to correctly identify and extract the most contextually relevant keyphrase. "}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Adaptation to Different Languages: "},{"type":"span","value":"A challenge in keyphrase extraction is smoothly transferring learned phrases between languages. Since each language has its own rules, techniques like POS tagging don't work well across languages. Even language embedding models need retraining. This requires specific methods for each language and constant reassessment to ensure effectiveness across different languages."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Adaptation to a new domain: "},{"type":"span","value":"This challenge is common in supervised keyphrase extraction models. They find it hard to apply what they learn from one knowledge domain to another because each domain has unique keyphrases. While unsupervised models may help with domain-specific issues, past "},{"url":"https://aclanthology.org/P17-1054/","type":"link","children":[{"type":"span","marks":["underline"],"value":"research"}]},{"type":"span","value":" shows that supervised models often perform better than unsupervised ones."}]}]}]},{"type":"heading","level":2,"children":[{"type":"span","value":"Real-world Applications"}]},{"type":"paragraph","children":[{"type":"span","value":"Keyphrase extraction is applied to various industries. The following are a couple of applications:"}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Search Engine Optimization (SEO) for Digital Content: "},{"type":"span","value":"Keyphrases are essential for enhancing by improving how content ranks on search engines. A keyphrase extraction algorithm helps find relevant keyphrases, which can be added to metadata, used as alternative text for images, and inform ad creation on platforms like "},{"url":"https://ads.google.com/home/","type":"link","children":[{"type":"span","marks":["underline"],"value":"Google Ads"}]},{"type":"span","value":". Utilizing keyphrases improves the SEO performance of content."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["strong","emphasis"],"value":"Business Intelligence through Customer Feedback Analysis: "},{"type":"span","value":"Keyphrase extraction helps businesses understand customer feedback from various sources like social media, surveys, and reviews. By analyzing these keyphrases, businesses can learn about customer sentiments and preferences. This helps them identify trends and patterns in feedback, revealing what aspects of their products or services are most important to customers."}]}]}]},{"type":"heading","level":2,"children":[{"type":"span","value":"Conclusion"}]},{"type":"paragraph","children":[{"type":"span","value":"In summary, keyphrase extraction is an important part of understanding written content. It's versatile, helping in summarizing, trend analysis, and decoding customer feedback, among other use cases. Despite challenges like language nuances and changing content, refining extraction techniques and exploring new metrics are essential. "}]},{"type":"paragraph","children":[{"type":"span","value":"With its power to uncover meaningful insights and improve information retrieval, keyphrase extraction sits at the intersection of language understanding and computational efficiency, revealing the essence of text with precision and clarity."}]},{"type":"paragraph","children":[{"item":"OcUPVLYOTdiOH90vXDPFbA","type":"inlineItem"}]}]}}},"_updatedAt":"2024-06-24T10:33:06-07:00"},"conversionPanel":{"__typename":"ComponentConversionPanelRecord","id":"181928448","internalName":"[Template Glossary] Conversion Panel","variant":"","heading":"Unlock language AI at scale with an API call.","headingType":"H2","desktopHeadingSize":"md","tabletHeadingSize":"md","mobileHeadingSize":"md","body":{"__typename":"ComponentConversionPanelModelBodyField","blocks":[],"links":[],"value":{"schema":"dast","document":{"type":"root","children":[{"type":"paragraph","children":[{"type":"span","value":"Get conversational intelligence with transcription and 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