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class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-Evaluation" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Evaluation"> <div class="vector-toc-text"> <span class="vector-toc-numb">4</span> <span>Evaluation</span> </div> </a> <ul id="toc-Evaluation-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-See_also" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#See_also"> <div class="vector-toc-text"> <span class="vector-toc-numb">5</span> <span>See also</span> </div> </a> <ul id="toc-See_also-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-References" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#References"> <div class="vector-toc-text"> <span class="vector-toc-numb">6</span> <span>References</span> </div> </a> <ul id="toc-References-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Further_reading" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#Further_reading"> <div class="vector-toc-text"> <span class="vector-toc-numb">7</span> <span>Further reading</span> </div> </a> <ul id="toc-Further_reading-sublist" class="vector-toc-list"> </ul> </li> </ul> </div> </div> </nav> </div> </div> <div class="mw-content-container"> <main id="content" class="mw-body"> <header class="mw-body-header vector-page-titlebar"> <nav aria-label="Contents" class="vector-toc-landmark"> <div id="vector-page-titlebar-toc" class="vector-dropdown vector-page-titlebar-toc vector-button-flush-left" > <input type="checkbox" id="vector-page-titlebar-toc-checkbox" role="button" aria-haspopup="true" data-event-name="ui.dropdown-vector-page-titlebar-toc" class="vector-dropdown-checkbox " aria-label="Toggle the table of contents" > <label id="vector-page-titlebar-toc-label" for="vector-page-titlebar-toc-checkbox" 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Available in 21 languages" > <label id="p-lang-btn-label" for="p-lang-btn-checkbox" class="vector-dropdown-label cdx-button cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--weight-quiet cdx-button--action-progressive mw-portlet-lang-heading-21" aria-hidden="true" ><span class="vector-icon mw-ui-icon-language-progressive mw-ui-icon-wikimedia-language-progressive"></span> <span class="vector-dropdown-label-text">21 languages</span> </label> <div class="vector-dropdown-content"> <div class="vector-menu-content"> <ul class="vector-menu-content-list"> <li class="interlanguage-link interwiki-ar mw-list-item"><a href="https://ar.wikipedia.org/wiki/%D8%AA%D9%88%D9%84%D9%8A%D8%AF_%D8%A7%D9%84%D9%84%D8%BA%D8%A7%D8%AA_%D8%A7%D9%84%D8%B7%D8%A8%D9%8A%D8%B9%D9%8A%D8%A9" title="توليد اللغات الطبيعية – Arabic" lang="ar" hreflang="ar" data-title="توليد اللغات الطبيعية" data-language-autonym="العربية" data-language-local-name="Arabic" class="interlanguage-link-target"><span>العربية</span></a></li><li class="interlanguage-link interwiki-bn mw-list-item"><a href="https://bn.wikipedia.org/wiki/%E0%A6%B8%E0%A7%8D%E0%A6%AC%E0%A6%BE%E0%A6%AD%E0%A6%BE%E0%A6%AC%E0%A6%BF%E0%A6%95_%E0%A6%AD%E0%A6%BE%E0%A6%B7%E0%A6%BE_%E0%A6%89%E0%A7%8E%E0%A6%AA%E0%A6%BE%E0%A6%A6%E0%A6%A8" title="স্বাভাবিক ভাষা উৎপাদন – Bangla" lang="bn" hreflang="bn" data-title="স্বাভাবিক ভাষা উৎপাদন" data-language-autonym="বাংলা" data-language-local-name="Bangla" class="interlanguage-link-target"><span>বাংলা</span></a></li><li class="interlanguage-link interwiki-ca mw-list-item"><a href="https://ca.wikipedia.org/wiki/Generaci%C3%B3_de_llenguatge_natural" title="Generació de llenguatge natural – Catalan" lang="ca" hreflang="ca" data-title="Generació de llenguatge natural" data-language-autonym="Català" data-language-local-name="Catalan" class="interlanguage-link-target"><span>Català</span></a></li><li class="interlanguage-link interwiki-de mw-list-item"><a href="https://de.wikipedia.org/wiki/Textgenerierung" title="Textgenerierung – German" lang="de" hreflang="de" data-title="Textgenerierung" data-language-autonym="Deutsch" data-language-local-name="German" class="interlanguage-link-target"><span>Deutsch</span></a></li><li class="interlanguage-link interwiki-es mw-list-item"><a href="https://es.wikipedia.org/wiki/Generaci%C3%B3n_de_lenguajes_naturales" title="Generación de lenguajes naturales – Spanish" lang="es" hreflang="es" data-title="Generación de lenguajes naturales" data-language-autonym="Español" data-language-local-name="Spanish" class="interlanguage-link-target"><span>Español</span></a></li><li class="interlanguage-link interwiki-eu mw-list-item"><a href="https://eu.wikipedia.org/wiki/Testu-sorkuntza_automatiko" title="Testu-sorkuntza automatiko – Basque" lang="eu" hreflang="eu" data-title="Testu-sorkuntza automatiko" data-language-autonym="Euskara" data-language-local-name="Basque" class="interlanguage-link-target"><span>Euskara</span></a></li><li class="interlanguage-link interwiki-fa mw-list-item"><a href="https://fa.wikipedia.org/wiki/%D8%AA%D9%88%D9%84%DB%8C%D8%AF_%D8%B2%D8%A8%D8%A7%D9%86_%D8%B7%D8%A8%DB%8C%D8%B9%DB%8C" title="تولید زبان طبیعی – Persian" lang="fa" hreflang="fa" data-title="تولید زبان طبیعی" data-language-autonym="فارسی" data-language-local-name="Persian" class="interlanguage-link-target"><span>فارسی</span></a></li><li class="interlanguage-link interwiki-fr mw-list-item"><a href="https://fr.wikipedia.org/wiki/G%C3%A9n%C3%A9ration_automatique_de_texte" title="Génération automatique de texte – French" lang="fr" hreflang="fr" data-title="Génération automatique de texte" data-language-autonym="Français" data-language-local-name="French" class="interlanguage-link-target"><span>Français</span></a></li><li class="interlanguage-link interwiki-ko mw-list-item"><a href="https://ko.wikipedia.org/wiki/%EC%9E%90%EC%97%B0%EC%96%B4_%EC%83%9D%EC%84%B1" title="자연어 생성 – Korean" lang="ko" hreflang="ko" data-title="자연어 생성" data-language-autonym="한국어" data-language-local-name="Korean" class="interlanguage-link-target"><span>한국어</span></a></li><li class="interlanguage-link interwiki-id mw-list-item"><a href="https://id.wikipedia.org/wiki/Penghasil_bahasa_alami" title="Penghasil bahasa alami – Indonesian" lang="id" hreflang="id" data-title="Penghasil bahasa alami" data-language-autonym="Bahasa Indonesia" data-language-local-name="Indonesian" class="interlanguage-link-target"><span>Bahasa Indonesia</span></a></li><li class="interlanguage-link interwiki-lt mw-list-item"><a href="https://lt.wikipedia.org/wiki/Nat%C5%ABralios_kalbos_generavimas" title="Natūralios kalbos generavimas – Lithuanian" lang="lt" hreflang="lt" data-title="Natūralios kalbos generavimas" data-language-autonym="Lietuvių" data-language-local-name="Lithuanian" class="interlanguage-link-target"><span>Lietuvių</span></a></li><li class="interlanguage-link interwiki-ja mw-list-item"><a href="https://ja.wikipedia.org/wiki/%E8%87%AA%E7%84%B6%E8%A8%80%E8%AA%9E%E7%94%9F%E6%88%90" title="自然言語生成 – Japanese" lang="ja" hreflang="ja" data-title="自然言語生成" data-language-autonym="日本語" data-language-local-name="Japanese" class="interlanguage-link-target"><span>日本語</span></a></li><li class="interlanguage-link interwiki-ru mw-list-item"><a href="https://ru.wikipedia.org/wiki/%D0%93%D0%B5%D0%BD%D0%B5%D1%80%D0%B0%D1%82%D0%BE%D1%80_%D1%82%D0%B5%D0%BA%D1%81%D1%82%D0%B0" title="Генератор текста – Russian" lang="ru" hreflang="ru" data-title="Генератор текста" data-language-autonym="Русский" data-language-local-name="Russian" class="interlanguage-link-target"><span>Русский</span></a></li><li class="interlanguage-link interwiki-simple mw-list-item"><a href="https://simple.wikipedia.org/wiki/Natural_language_generation" title="Natural language generation – Simple English" lang="en-simple" hreflang="en-simple" data-title="Natural language generation" data-language-autonym="Simple English" data-language-local-name="Simple English" class="interlanguage-link-target"><span>Simple English</span></a></li><li class="interlanguage-link interwiki-sr mw-list-item"><a href="https://sr.wikipedia.org/wiki/%D0%93%D0%B5%D0%BD%D0%B5%D1%80%D0%B8%D1%81%D0%B0%D1%9A%D0%B5_%D0%BF%D1%80%D0%B8%D1%80%D0%BE%D0%B4%D0%BD%D0%BE%D0%B3_%D1%98%D0%B5%D0%B7%D0%B8%D0%BA%D0%B0" title="Генерисање природног језика – Serbian" lang="sr" hreflang="sr" data-title="Генерисање природног језика" data-language-autonym="Српски / srpski" data-language-local-name="Serbian" class="interlanguage-link-target"><span>Српски / srpski</span></a></li><li class="interlanguage-link interwiki-th mw-list-item"><a href="https://th.wikipedia.org/wiki/%E0%B8%81%E0%B8%B2%E0%B8%A3%E0%B8%81%E0%B9%88%E0%B8%AD%E0%B8%81%E0%B8%B3%E0%B9%80%E0%B8%99%E0%B8%B4%E0%B8%94%E0%B8%A0%E0%B8%B2%E0%B8%A9%E0%B8%B2%E0%B8%98%E0%B8%A3%E0%B8%A3%E0%B8%A1%E0%B8%8A%E0%B8%B2%E0%B8%95%E0%B8%B4" title="การก่อกำเนิดภาษาธรรมชาติ – Thai" lang="th" hreflang="th" data-title="การก่อกำเนิดภาษาธรรมชาติ" data-language-autonym="ไทย" data-language-local-name="Thai" class="interlanguage-link-target"><span>ไทย</span></a></li><li class="interlanguage-link interwiki-tr mw-list-item"><a href="https://tr.wikipedia.org/wiki/Do%C4%9Fal_dil_%C3%BCretme" title="Doğal dil üretme – Turkish" lang="tr" hreflang="tr" data-title="Doğal dil üretme" data-language-autonym="Türkçe" data-language-local-name="Turkish" class="interlanguage-link-target"><span>Türkçe</span></a></li><li class="interlanguage-link interwiki-uk mw-list-item"><a href="https://uk.wikipedia.org/wiki/%D0%93%D0%B5%D0%BD%D0%B5%D1%80%D1%83%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D0%BF%D1%80%D0%B8%D1%80%D0%BE%D0%B4%D0%BD%D0%BE%D1%97_%D0%BC%D0%BE%D0%B2%D0%B8" title="Генерування природної мови – Ukrainian" lang="uk" hreflang="uk" data-title="Генерування природної мови" data-language-autonym="Українська" data-language-local-name="Ukrainian" class="interlanguage-link-target"><span>Українська</span></a></li><li class="interlanguage-link interwiki-vi mw-list-item"><a href="https://vi.wikipedia.org/wiki/Sinh_ng%C3%B4n_ng%E1%BB%AF_t%E1%BB%B1_nhi%C3%AAn" title="Sinh ngôn ngữ tự nhiên – Vietnamese" lang="vi" hreflang="vi" data-title="Sinh ngôn ngữ tự nhiên" data-language-autonym="Tiếng Việt" data-language-local-name="Vietnamese" class="interlanguage-link-target"><span>Tiếng Việt</span></a></li><li class="interlanguage-link interwiki-zh-yue mw-list-item"><a href="https://zh-yue.wikipedia.org/wiki/%E8%87%AA%E7%84%B6%E8%AA%9E%E8%A8%80%E7%94%9F%E6%88%90" title="自然語言生成 – Cantonese" lang="yue" hreflang="yue" data-title="自然語言生成" data-language-autonym="粵語" data-language-local-name="Cantonese" class="interlanguage-link-target"><span>粵語</span></a></li><li class="interlanguage-link interwiki-zh mw-list-item"><a href="https://zh.wikipedia.org/wiki/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E7%94%9F%E6%88%90" title="自然语言生成 – Chinese" lang="zh" 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.hatnote+link+.hatnote{margin-top:-0.5em}@media print{body.ns-0 .mw-parser-output .hatnote{display:none!important}}</style><div role="note" class="hatnote navigation-not-searchable">This article is about the computer processing ability. For the psychological concepts, see <a href="/wiki/Origin_of_language" title="Origin of language">Origin of language</a> or <a href="/wiki/Universal_grammar" title="Universal grammar">Universal grammar</a></div><p><b>Natural language generation</b> (<b>NLG</b>) is a software process that produces <a href="/wiki/Natural_language" title="Natural language">natural language</a> output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems that can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information".<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> </p><p>While it is widely agreed that the output of any NLG process is text, there is some disagreement about whether the inputs of an NLG system need to be non-linguistic.<sup id="cite_ref-Gatt_2-0" class="reference"><a href="#cite_note-Gatt-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> Common applications of NLG methods include the production of various reports, for example weather <sup id="cite_ref-fog_3-0" class="reference"><a href="#cite_note-fog-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> and patient reports;<sup id="cite_ref-portet_4-0" class="reference"><a href="#cite_note-portet-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> image captions;<sup id="cite_ref-farhadi_5-0" class="reference"><a href="#cite_note-farhadi-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> and <a href="/wiki/Chatbot" title="Chatbot">chatbots</a> like <a href="/wiki/ChatGPT" title="ChatGPT">chatGPT</a>. </p><p>Automated NLG can be compared to the process humans use when they turn ideas into writing or speech. <a href="/wiki/Psycholinguists" class="mw-redirect" title="Psycholinguists">Psycholinguists</a> prefer the term <a href="/wiki/Language_production" title="Language production">language production</a> for this process, which can also be described in mathematical terms, or modeled in a computer for psychological research. NLG systems can also be compared to <a href="/wiki/Translator_(computing)" title="Translator (computing)">translators</a> of artificial computer languages, such as <a href="/wiki/Decompiler" title="Decompiler">decompilers</a> or <a href="/wiki/Transpiler" class="mw-redirect" title="Transpiler">transpilers</a>, which also produce human-readable code generated from an <a href="/wiki/Intermediate_representation" title="Intermediate representation">intermediate representation</a>. Human languages tend to be considerably more complex and allow for much more ambiguity and variety of expression than programming languages, which makes NLG more challenging. </p><p>NLG may be viewed as complementary to <a href="/wiki/Natural-language_understanding" class="mw-redirect" title="Natural-language understanding">natural-language understanding</a> (NLU): whereas in natural-language understanding, the system needs to disambiguate the input sentence to produce the machine representation language, in NLG the system needs to make decisions about how to put a representation into words. The practical considerations in building NLU vs. NLG systems are not symmetrical. NLU needs to deal with ambiguous or erroneous user input, whereas the ideas the system wants to express through NLG are generally known precisely. NLG needs to choose a specific, self-consistent textual representation from many potential representations, whereas NLU generally tries to produce a single, normalized representation of the idea expressed.<sup id="cite_ref-Ehud_6-0" class="reference"><a href="#cite_note-Ehud-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><p>NLG has existed since <a href="/wiki/ELIZA" title="ELIZA">ELIZA</a> was developed in the mid 1960s, but the methods were first used commercially in the 1990s.<sup id="cite_ref-ehud-history_7-0" class="reference"><a href="#cite_note-ehud-history-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> NLG techniques range from simple template-based systems like a <a href="/wiki/Mail_merge" title="Mail merge">mail merge</a> that generates <a href="/wiki/Form_letter" title="Form letter">form letters</a>, to systems that have a complex understanding of human grammar. NLG can also be accomplished by training a statistical model using <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a>, typically on a large <a href="/wiki/Text_corpus" title="Text corpus">corpus</a> of human-written texts.<sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Example">Example</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=1" title="Edit section: Example"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The <i>Pollen Forecast for Scotland</i> system<sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> is a simple example of a simple NLG system that could essentially be based on a template. This system takes as input six numbers, which give predicted pollen levels in different parts of Scotland. From these numbers, the system generates a short textual summary of pollen levels as its output. </p><p>For example, using the historical data for July 1, 2005, the software produces: </p> <blockquote> <p>Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country. However, in Northern areas, pollen levels will be moderate with values of 4. </p> </blockquote> <p>In contrast, the actual forecast (written by a human meteorologist) from this data was: </p> <blockquote> <p>Pollen counts are expected to remain high at level 6 over most of Scotland, and even level 7 in the south east. The only relief is in the Northern Isles and far northeast of mainland Scotland with medium levels of pollen count. </p> </blockquote> <p>Comparing these two illustrates some of the choices that NLG systems must make; these are further discussed below. </p> <div class="mw-heading mw-heading2"><h2 id="Stages">Stages</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=2" title="Edit section: Stages"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The process to generate text can be as simple as keeping a list of canned text that is copied and pasted, possibly linked with some glue text. The results may be satisfactory in simple domains such as horoscope machines or generators of personalized business letters. However, a sophisticated NLG system needs to include stages of planning and merging of information to enable the generation of text that looks natural and does not become repetitive. The typical stages of natural-language generation, as proposed by Dale and Reiter,<sup id="cite_ref-Ehud_6-1" class="reference"><a href="#cite_note-Ehud-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> are: </p><p><b><a href="/wiki/Content_determination" title="Content determination">Content determination</a></b>: Deciding what information to mention in the text. For instance, in the pollen example above, deciding whether to explicitly mention that pollen level is 7 in the southeast. </p><p><b><a href="/wiki/Document_structuring" title="Document structuring">Document structuring</a></b>: Overall organisation of the information to convey. For example, deciding to describe the areas with high pollen levels first, instead of the areas with low pollen levels. </p><p><b><a href="/wiki/Aggregation_(linguistics)" title="Aggregation (linguistics)">Aggregation</a></b>: Merging of similar sentences to improve readability and naturalness. For instance, merging the two following sentences: </p> <ul><li><i>Grass pollen levels for Friday have increased from the moderate to high levels of yesterday</i> and</li> <li><i>Grass pollen levels will be around 6 to 7 across most parts of the country</i></li></ul> <p>into the following single sentence: </p> <ul><li><i>Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country</i>.</li></ul> <p><b><a href="/wiki/Lexical_choice" title="Lexical choice">Lexical choice</a></b>: Putting words to the concepts. For example, deciding whether <i>medium</i> or <i>moderate</i> should be used when describing a pollen level of 4. </p><p><b><a href="/wiki/Referring_expression_generation" title="Referring expression generation">Referring expression generation</a></b>: Creating <a href="/wiki/Referring_expression" title="Referring expression">referring expressions</a> that identify objects and regions. For example, deciding to use <i>in the Northern Isles and far northeast of mainland Scotland</i> to refer to a certain region in Scotland. This task also includes making decisions about <a href="/wiki/Pronouns" class="mw-redirect" title="Pronouns">pronouns</a> and other types of <a href="/wiki/Anaphora_(linguistics)" title="Anaphora (linguistics)">anaphora</a>. </p><p><b><a href="/wiki/Realization_(linguistics)" title="Realization (linguistics)">Realization</a></b>: Creating the actual text, which should be correct according to the rules of <a href="/wiki/Syntax" title="Syntax">syntax</a>, <a href="/wiki/Morphology_(linguistics)" title="Morphology (linguistics)">morphology</a>, and <a href="/wiki/Orthography" title="Orthography">orthography</a>. For example, using <i>will be</i> for the future tense of <i>to be</i>. </p><p>An alternative approach to NLG is to use "end-to-end" machine learning to build a system, without having separate stages as above.<sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> In other words, we build an NLG system by training a machine learning algorithm (often an <a href="/wiki/Long_short-term_memory" title="Long short-term memory">LSTM</a>) on a large data set of input data and corresponding (human-written) output texts. The end-to-end approach has perhaps been most successful in <a href="/wiki/Automatic_image_annotation" title="Automatic image annotation">image captioning</a>,<sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> that is automatically generating a textual caption for an image. </p> <div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=3" title="Edit section: Applications"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-heading mw-heading3"><h3 id="Automatic_report_generation">Automatic report generation</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=4" title="Edit section: Automatic report generation"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>From a commercial perspective, the most successful NLG applications have been <i>data-to-text</i> systems which <a href="/wiki/Automatic_summarization" title="Automatic summarization">generate textual summaries</a> of databases and data sets; these systems usually perform <a href="/wiki/Data_analysis" title="Data analysis">data analysis</a> as well as text generation. Research has shown that textual summaries can be more effective than graphs and other visuals for decision support,<sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-13" class="reference"><a href="#cite_note-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-14" class="reference"><a href="#cite_note-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> and that computer-generated texts can be superior (from the reader's perspective) to human-written texts.<sup id="cite_ref-15" class="reference"><a href="#cite_note-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup> </p><p>The first commercial data-to-text systems produced weather forecasts from weather data. The earliest such system to be deployed was FoG,<sup id="cite_ref-fog_3-1" class="reference"><a href="#cite_note-fog-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> which was used by Environment Canada to generate weather forecasts in French and English in the early 1990s. The success of FoG triggered other work, both research and commercial. Recent applications include the <a href="/wiki/Met_Office" title="Met Office">UK Met Office's</a> text-enhanced forecast.<sup id="cite_ref-16" class="reference"><a href="#cite_note-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> </p><p>Data-to-text systems have since been applied in a range of settings. Following the minor earthquake near Beverly Hills, California on March 17, 2014, The Los Angeles Times reported details about the time, location and strength of the quake within 3 minutes of the event. This report was automatically generated by a 'robo-journalist', which converted the incoming data into text via a preset template.<sup id="cite_ref-17" class="reference"><a href="#cite_note-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-18" class="reference"><a href="#cite_note-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> Currently there is considerable commercial interest in using NLG to summarise financial and business data. Indeed, <a href="/wiki/Gartner" title="Gartner">Gartner</a> has said that NLG will become a standard feature of 90% of modern BI and analytics platforms.<sup id="cite_ref-19" class="reference"><a href="#cite_note-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup> NLG is also being used commercially in <a href="/wiki/Automated_journalism" title="Automated journalism">automated journalism</a>, <a href="/wiki/Chatbot" title="Chatbot">chatbots</a>, generating product descriptions for e-commerce sites, summarising medical records,<sup id="cite_ref-20" class="reference"><a href="#cite_note-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-portet_4-1" class="reference"><a href="#cite_note-portet-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> and enhancing <a href="/wiki/Accessibility" title="Accessibility">accessibility</a> (for example by describing graphs and data sets to blind people<sup id="cite_ref-21" class="reference"><a href="#cite_note-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup>). </p><p>An example of an interactive use of NLG is the <a href="/wiki/WYSIWYM_(Meant)" class="mw-redirect" title="WYSIWYM (Meant)">WYSIWYM</a> framework. It stands for <i>What you see is what you meant</i> and allows users to see and manipulate the continuously rendered view (NLG output) of an underlying formal language document (NLG input), thereby editing the formal language without learning it. </p><p>Looking ahead, the current progress in data-to-text generation paves the way for tailoring texts to specific audiences. For example, data from babies in neonatal care can be converted into text differently in a clinical setting, with different levels of technical detail and explanatory language, depending on intended recipient of the text (doctor, nurse, patient). The same idea can be applied in a sports setting, with different reports generated for fans of specific teams.<sup id="cite_ref-:0_22-0" class="reference"><a href="#cite_note-:0-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Image_captioning">Image captioning</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=5" title="Edit section: Image captioning"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Over the past few years, there has been an increased interest in <a href="/wiki/Automatic_image_annotation" title="Automatic image annotation">automatically generating captions</a> for images, as part of a broader endeavor to investigate the interface between vision and language. A case of data-to-text generation, the algorithm of image captioning (or automatic image description) involves taking an image, analyzing its visual content, and generating a textual description (typically a sentence) that verbalizes the most prominent aspects of the image. </p><p>An image captioning system involves two sub-tasks. In Image Analysis, features and attributes of an image are detected and labelled, before mapping these outputs to linguistic structures. Recent research utilize<i>s</i> deep learning approaches through features from a pre-trained <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural network</a> such as AlexNet, VGG or Caffe, where caption generators use an activation layer from the pre-trained network as their input features. Text Generation, the second task, is performed using a wide range of techniques. For example, in the Midge system, input images are represented as triples consisting of object/stuff detections, action/<a href="/wiki/Pose_(computer_vision)" title="Pose (computer vision)">pose</a> detections and spatial relations. These are subsequently mapped to <noun, verb, preposition> triples and realized using a tree substitution grammar.<sup id="cite_ref-:0_22-1" class="reference"><a href="#cite_note-:0-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> </p><p>A common method in image captioning is to use a vision model (such as a <a href="/wiki/Residual_neural_network" title="Residual neural network">ResNet</a>) to encode an image into a vector, then use a language model (such as an <a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">RNN</a>) to decode the vector into a caption.<sup id="cite_ref-23" class="reference"><a href="#cite_note-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-24" class="reference"><a href="#cite_note-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup> </p><p>Despite advancements, challenges and opportunities remain in image capturing research. Notwithstanding the recent introduction of Flickr30K, MS COCO and other large datasets <i>have</i> enabled the training of more complex models such as neural networks, it has been argued that <i>research in image captioning could benefit from larger and diversified datasets.</i> Designing automatic measures that can mimic human judgments in evaluating the suitability of image descriptions is another need in the area. Other open challenges include visual <a href="/wiki/Question_answering" title="Question answering">question-answering</a> (VQA),<sup id="cite_ref-25" class="reference"><a href="#cite_note-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> as well as the construction and evaluation multilingual repositories for image description.<sup id="cite_ref-:0_22-2" class="reference"><a href="#cite_note-:0-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Chatbots">Chatbots</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=6" title="Edit section: Chatbots"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Another area where NLG has been widely applied is automated <a href="/wiki/Dialogue" title="Dialogue">dialogue</a> systems, frequently in the form of chatbots. A <a href="/wiki/Chatbot" title="Chatbot">chatbot</a> or chatterbot is a <a href="/wiki/Software_agent" title="Software agent">software</a> application used to conduct an on-line chat <a href="/wiki/Conversation" title="Conversation">conversation</a> via text or <a href="/wiki/Speech_synthesis" title="Speech synthesis">text-to-speech</a>, in lieu of providing direct contact with a live human agent. While <a href="/wiki/Natural_language_processing" title="Natural language processing">natural language processing</a> (NLP) techniques are applied in deciphering human input, NLG informs the output part of the chatbot algorithms in facilitating real-time dialogues. </p><p>Early chatbot systems, including <a href="/wiki/Cleverbot" title="Cleverbot">Cleverbot</a> created by Rollo Carpenter in 1988 and published in 1997,<sup class="noprint Inline-Template Template-Fact" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed"><span title="This claim needs references to reliable sources. (January 2023)">citation needed</span></a></i>]</sup> reply to questions by identifying how a human has responded to the same question in a conversation database using <a href="/wiki/Information_retrieval" title="Information retrieval">information retrieval</a> (IR) techniques.<sup class="noprint Inline-Template Template-Fact" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed"><span title="This claim needs references to reliable sources. (January 2023)">citation needed</span></a></i>]</sup> Modern chatbot systems predominantly rely on machine learning (ML) models, such as sequence-to-sequence learning and reinforcement learning to generate natural language output. Hybrid models have also been explored. For example, the Alibaba shopping assistant first uses an IR approach to retrieve the best candidates from the knowledge base, then uses the ML-driven seq2seq model re-rank the candidate responses and generate the answer.<sup id="cite_ref-26" class="reference"><a href="#cite_note-26"><span class="cite-bracket">[</span>26<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Creative_writing_and_computational_humor">Creative writing and computational humor</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=7" title="Edit section: Creative writing and computational humor"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Creative language generation by NLG has been hypothesized since the field's origins. A recent pioneer in the area is Phillip Parker, who has developed an arsenal of algorithms capable of automatically generating textbooks, crossword puzzles, poems and books on topics ranging from bookbinding to cataracts.<sup id="cite_ref-27" class="reference"><a href="#cite_note-27"><span class="cite-bracket">[</span>27<span class="cite-bracket">]</span></a></sup> The advent of large pretrained transformer-based language models such as GPT-3 has also enabled breakthroughs, with such models demonstrating recognizable ability for creating-writing tasks.<sup id="cite_ref-28" class="reference"><a href="#cite_note-28"><span class="cite-bracket">[</span>28<span class="cite-bracket">]</span></a></sup> </p><p>A related area of NLG application is computational humor production.  JAPE (Joke Analysis and Production Engine) is one of the earliest large, automated humor production systems that uses a hand-coded template-based approach to create punning riddles for children. HAHAcronym creates humorous reinterpretations of any given acronym, as well as proposing new fitting acronyms given some keywords.<sup id="cite_ref-:1_29-0" class="reference"><a href="#cite_note-:1-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> </p><p>Despite progresses, many challenges remain in producing automated creative and humorous content that rival human output. In an experiment for generating satirical headlines, outputs of their best BERT-based model were perceived as funny 9.4% of the time (while real headlines from <a href="/wiki/The_Onion" title="The Onion">The Onion</a> were 38.4%) and a GPT-2 model fine-tuned on satirical headlines achieved 6.9%.<sup id="cite_ref-30" class="reference"><a href="#cite_note-30"><span class="cite-bracket">[</span>30<span class="cite-bracket">]</span></a></sup>  It has been pointed out that two main issues with humor-generation systems are the lack of annotated data sets and the lack of formal evaluation methods,<sup id="cite_ref-:1_29-1" class="reference"><a href="#cite_note-:1-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> which could be applicable to other creative content generation. Some have argued relative to other applications, there has been a lack of attention to creative aspects of language production within NLG. NLG researchers stand to benefit from insights into what constitutes creative language production, as well as structural features of narrative that have the potential to improve NLG output even in data-to-text systems.<sup id="cite_ref-:0_22-3" class="reference"><a href="#cite_note-:0-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Evaluation">Evaluation</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=8" title="Edit section: Evaluation"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>As in other scientific fields, NLG researchers need to test how well their systems, modules, and algorithms work. This is called <i>evaluation</i>. There are three basic techniques for evaluating NLG systems: </p> <ul><li><i>Task-based (extrinsic) evaluation</i>: give the generated text to a person, and assess how well it helps them perform a task (or otherwise achieves its communicative goal). For example, a system which generates summaries of medical data can be evaluated by giving these summaries to doctors, and assessing whether the summaries help doctors make better decisions.<sup id="cite_ref-portet_4-2" class="reference"><a href="#cite_note-portet-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup></li> <li><i>Human ratings</i>: give the generated text to a person, and ask them to rate the quality and usefulness of the text.</li> <li><i>Metrics</i>: compare generated texts to texts written by people from the same input data, using an automatic metric such as <a href="/wiki/BLEU" title="BLEU">BLEU</a>, <a href="/wiki/METEOR" title="METEOR">METEOR</a>, <a href="/wiki/ROUGE_(metric)" title="ROUGE (metric)">ROUGE</a> and <a href="/wiki/LEPOR" title="LEPOR">LEPOR</a>.</li></ul> <p>An ultimate goal is how useful NLG systems are at helping people, which is the first of the above techniques. However, task-based evaluations are time-consuming and expensive, and can be difficult to carry out (especially if they require subjects with specialised expertise, such as doctors). Hence (as in other areas of NLP) task-based evaluations are the exception, not the norm. </p><p>Recently researchers are assessing how well human-ratings and metrics correlate with (predict) task-based evaluations. Work is being conducted in the context of Generation Challenges<sup id="cite_ref-31" class="reference"><a href="#cite_note-31"><span class="cite-bracket">[</span>31<span class="cite-bracket">]</span></a></sup> shared-task events. Initial results suggest that human ratings are much better than metrics in this regard. In other words, human ratings usually do predict task-effectiveness at least to some degree (although there are exceptions), while ratings produced by metrics often do not predict task-effectiveness well. These results are preliminary. In any case, human ratings are the most popular evaluation technique in NLG; this is contrast to <a href="/wiki/Evaluation_of_machine_translation" title="Evaluation of machine translation">machine translation</a>, where metrics are widely used. </p><p>An AI can be graded on <i>faithfulness</i> to its training data or, alternatively, on <i>factuality</i>. A response that reflects the training data but not reality is faithful but not factual. A confident but unfaithful response is a <i><a href="/wiki/Hallucination_(artificial_intelligence)" title="Hallucination (artificial intelligence)">hallucination</a></i>. In Natural Language Processing, a hallucination is often defined as "generated content that is nonsensical or unfaithful to the provided source content".<sup id="cite_ref-32" class="reference"><a href="#cite_note-32"><span class="cite-bracket">[</span>32<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=9" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/Autocompletion" class="mw-redirect" title="Autocompletion">Autocompletion</a></li> <li><a href="/wiki/Automated_journalism" title="Automated journalism">Automated journalism</a></li> <li><a href="/wiki/Automated_paraphrasing" class="mw-redirect" title="Automated paraphrasing">Automated paraphrasing</a></li> <li><a href="/wiki/Markov_text_generators" class="mw-redirect" title="Markov text generators">Markov text generators</a></li> <li><a href="/wiki/Meaning-text_theory" class="mw-redirect" title="Meaning-text theory">Meaning-text theory</a></li> <li><a href="/wiki/Generative_art#Literature" title="Generative art">Generative art § Literature</a></li></ul> <div class="mw-heading mw-heading2"><h2 id="References">References</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Natural_language_generation&action=edit&section=10" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output 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.citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/a/aa/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/4/4c/Wikisource-logo.svg")right 0.1em center/12px no-repeat}body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-free a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-limited a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-registration a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-subscription a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .cs1-ws-icon a{background-size:contain;padding:0 1em 0 0}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:var(--color-error,#d33)}.mw-parser-output .cs1-visible-error{color:var(--color-error,#d33)}.mw-parser-output .cs1-maint{display:none;color:#085;margin-left:0.3em}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}@media screen{.mw-parser-output .cs1-format{font-size:95%}html.skin-theme-clientpref-night .mw-parser-output .cs1-maint{color:#18911f}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output 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Cambridge, UK: Cambridge University Press. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-521-02451-8" title="Special:BookSources/978-0-521-02451-8"><bdi>978-0-521-02451-8</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Building+natural+language+generation+systems&rft.place=Cambridge%2C+UK&rft.pub=Cambridge+University+Press&rft.date=2000&rft.isbn=978-0-521-02451-8&rft.au=Dale%2C+Robert&rft.au=Reiter%2C+Ehud&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+generation" class="Z3988"></span></li> <li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFEvans,_RogerPiwek,_PaulCahill,_Lynne2002" class="citation conference cs1">Evans, Roger; Piwek, Paul; Cahill, Lynne (2002). <i>What is NLG?</i>. INLG2002. New York, US.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=conference&rft.btitle=What+is+NLG%3F&rft.place=New+York%2C+US&rft.date=2002&rft.au=Evans%2C+Roger&rft.au=Piwek%2C+Paul&rft.au=Cahill%2C+Lynne&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+generation" class="Z3988"></span> <a rel="nofollow" class="external text" href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.3015">paper</a></li> <li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFGatt,_AlbertKrahmer,_Emiel2018" class="citation journal cs1">Gatt, Albert; Krahmer, Emiel (2018). <a rel="nofollow" class="external text" href="https://www.jair.org/index.php/jair/article/view/11173">"Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation"</a>. <i>Journal of Artificial Intelligence Research</i>. <b>61</b>: 65–170. <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1703.09902">1703.09902</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1613%2Fjair.5477">10.1613/jair.5477</a>. <a href="/wiki/S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:16946362">16946362</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Journal+of+Artificial+Intelligence+Research&rft.atitle=Survey+of+the+State+of+the+Art+in+Natural+Language+Generation%3A+Core+tasks%2C+applications+and+evaluation&rft.volume=61&rft.pages=65-170&rft.date=2018&rft_id=info%3Aarxiv%2F1703.09902&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A16946362%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1613%2Fjair.5477&rft.au=Gatt%2C+Albert&rft.au=Krahmer%2C+Emiel&rft_id=https%3A%2F%2Fwww.jair.org%2Findex.php%2Fjair%2Farticle%2Fview%2F11173&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+generation" class="Z3988"></span></li> <li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFReiter,_Ehud2018" class="citation web cs1">Reiter, Ehud (2018-01-16). <a rel="nofollow" class="external text" href="https://ehudreiter.com/2018/01/16/learn-about-nlg/">"How do I Learn about NLG?"</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=unknown&rft.btitle=How+do+I+Learn+about+NLG%3F&rft.date=2018-01-16&rft.au=Reiter%2C+Ehud&rft_id=https%3A%2F%2Fehudreiter.com%2F2018%2F01%2F16%2Flearn-about-nlg%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+generation" class="Z3988"></span></li></ul> <p><br /> </p> <div class="navbox-styles"><style data-mw-deduplicate="TemplateStyles:r1129693374">.mw-parser-output .hlist dl,.mw-parser-output .hlist ol,.mw-parser-output .hlist ul{margin:0;padding:0}.mw-parser-output .hlist dd,.mw-parser-output .hlist dt,.mw-parser-output .hlist li{margin:0;display:inline}.mw-parser-output .hlist.inline,.mw-parser-output .hlist.inline dl,.mw-parser-output .hlist.inline ol,.mw-parser-output .hlist.inline ul,.mw-parser-output .hlist dl dl,.mw-parser-output .hlist dl ol,.mw-parser-output .hlist dl ul,.mw-parser-output .hlist ol dl,.mw-parser-output .hlist ol ol,.mw-parser-output .hlist ol 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template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Natural_language_processing" title="Special:EditPage/Template:Natural language processing"><abbr title="Edit this template">e</abbr></a></li></ul></div><div id="Natural_language_processing" style="font-size:114%;margin:0 4em"><a href="/wiki/Natural_language_processing" title="Natural language processing">Natural language processing</a></div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%">General terms</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AI-complete" title="AI-complete">AI-complete</a></li> <li><a href="/wiki/Bag-of-words_model" title="Bag-of-words model">Bag-of-words</a></li> <li><a href="/wiki/N-gram" title="N-gram">n-gram</a> <ul><li><a href="/wiki/Bigram" title="Bigram">Bigram</a></li> <li><a href="/wiki/Trigram" title="Trigram">Trigram</a></li></ul></li> <li><a href="/wiki/Computational_linguistics" title="Computational linguistics">Computational linguistics</a></li> <li><a href="/wiki/Natural_language_understanding" title="Natural language understanding">Natural language understanding</a></li> <li><a href="/wiki/Stop_word" title="Stop word">Stop words</a></li> <li><a href="/wiki/Text_processing" title="Text processing">Text processing</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Text_mining" title="Text mining">Text analysis</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Argument_mining" title="Argument mining">Argument mining</a></li> <li><a href="/wiki/Collocation_extraction" title="Collocation extraction">Collocation extraction</a></li> <li><a href="/wiki/Concept_mining" title="Concept mining">Concept mining</a></li> <li><a href="/wiki/Coreference#Coreference_resolution" title="Coreference">Coreference resolution</a></li> <li><a href="/wiki/Deep_linguistic_processing" title="Deep linguistic processing">Deep linguistic processing</a></li> <li><a href="/wiki/Distant_reading" title="Distant reading">Distant reading</a></li> <li><a href="/wiki/Information_extraction" title="Information extraction">Information extraction</a></li> <li><a href="/wiki/Named-entity_recognition" title="Named-entity recognition">Named-entity recognition</a></li> <li><a href="/wiki/Ontology_learning" title="Ontology learning">Ontology learning</a></li> <li><a href="/wiki/Parsing" title="Parsing">Parsing</a> <ul><li><a href="/wiki/Semantic_parsing" title="Semantic parsing">Semantic parsing</a></li> <li><a href="/wiki/Syntactic_parsing_(computational_linguistics)" title="Syntactic parsing (computational linguistics)">Syntactic parsing</a></li></ul></li> <li><a href="/wiki/Part-of-speech_tagging" title="Part-of-speech tagging">Part-of-speech tagging</a></li> <li><a href="/wiki/Semantic_analysis_(machine_learning)" title="Semantic analysis (machine learning)">Semantic analysis</a></li> <li><a href="/wiki/Semantic_role_labeling" title="Semantic role labeling">Semantic role labeling</a></li> <li><a href="/wiki/Semantic_decomposition_(natural_language_processing)" title="Semantic decomposition (natural language processing)">Semantic decomposition</a></li> <li><a href="/wiki/Semantic_similarity" title="Semantic similarity">Semantic similarity</a></li> <li><a href="/wiki/Sentiment_analysis" title="Sentiment analysis">Sentiment analysis</a></li></ul> <ul><li><a href="/wiki/Terminology_extraction" title="Terminology extraction">Terminology extraction</a></li> <li><a href="/wiki/Text_mining" title="Text mining">Text mining</a></li> <li><a href="/wiki/Textual_entailment" title="Textual entailment">Textual entailment</a></li> <li><a href="/wiki/Truecasing" title="Truecasing">Truecasing</a></li> <li><a href="/wiki/Word-sense_disambiguation" title="Word-sense disambiguation">Word-sense disambiguation</a></li> <li><a href="/wiki/Word-sense_induction" title="Word-sense induction">Word-sense induction</a></li></ul> </div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th id="Text_segmentation" scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Text_segmentation" title="Text segmentation">Text segmentation</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Compound-term_processing" title="Compound-term processing">Compound-term processing</a></li> <li><a href="/wiki/Lemmatisation" class="mw-redirect" title="Lemmatisation">Lemmatisation</a></li> <li><a href="/wiki/Lexical_analysis" title="Lexical analysis">Lexical analysis</a></li> <li><a href="/wiki/Shallow_parsing" title="Shallow parsing">Text chunking</a></li> <li><a href="/wiki/Stemming" title="Stemming">Stemming</a></li> <li><a href="/wiki/Sentence_boundary_disambiguation" title="Sentence boundary disambiguation">Sentence segmentation</a></li> <li><a href="/wiki/Word#Word_boundaries" title="Word">Word segmentation</a></li></ul> </div></td></tr></tbody></table><div> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Automatic_summarization" title="Automatic summarization">Automatic summarization</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Multi-document_summarization" title="Multi-document summarization">Multi-document summarization</a></li> <li><a href="/wiki/Sentence_extraction" title="Sentence extraction">Sentence extraction</a></li> <li><a href="/wiki/Text_simplification" title="Text simplification">Text simplification</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Machine_translation" title="Machine translation">Machine translation</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Computer-assisted_translation" title="Computer-assisted translation">Computer-assisted</a></li> <li><a href="/wiki/Example-based_machine_translation" title="Example-based machine translation">Example-based</a></li> <li><a href="/wiki/Rule-based_machine_translation" title="Rule-based machine translation">Rule-based</a></li> <li><a href="/wiki/Statistical_machine_translation" title="Statistical machine translation">Statistical</a></li> <li><a href="/wiki/Transfer-based_machine_translation" title="Transfer-based machine translation">Transfer-based</a></li> <li><a href="/wiki/Neural_machine_translation" title="Neural machine translation">Neural</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Distributional_semantics" title="Distributional semantics">Distributional semantics</a> models</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/BERT_(language_model)" title="BERT (language model)">BERT</a></li> <li><a href="/wiki/Document-term_matrix" title="Document-term matrix">Document-term matrix</a></li> <li><a href="/wiki/Explicit_semantic_analysis" title="Explicit semantic analysis">Explicit semantic analysis</a></li> <li><a href="/wiki/FastText" title="FastText">fastText</a></li> <li><a href="/wiki/GloVe" title="GloVe">GloVe</a></li> <li><a href="/wiki/Language_model" title="Language model">Language model</a> (<a href="/wiki/Large_language_model" title="Large language model">large</a>)</li> <li><a href="/wiki/Latent_semantic_analysis" title="Latent semantic analysis">Latent semantic analysis</a></li> <li><a href="/wiki/Seq2seq" title="Seq2seq">Seq2seq</a></li> <li><a href="/wiki/Word_embedding" title="Word embedding">Word embedding</a></li> <li><a href="/wiki/Word2vec" title="Word2vec">Word2vec</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Language_resource" title="Language resource">Language resources</a>,<br />datasets and corpora</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Types and<br />standards</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Corpus_linguistics" title="Corpus linguistics">Corpus linguistics</a></li> <li><a href="/wiki/Lexical_resource" title="Lexical resource">Lexical resource</a></li> <li><a href="/wiki/Linguistic_Linked_Open_Data" title="Linguistic Linked Open Data">Linguistic Linked Open Data</a></li> <li><a href="/wiki/Machine-readable_dictionary" title="Machine-readable dictionary">Machine-readable dictionary</a></li> <li><a href="/wiki/Parallel_text" title="Parallel text">Parallel text</a></li> <li><a href="/wiki/PropBank" title="PropBank">PropBank</a></li> <li><a href="/wiki/Semantic_network" title="Semantic network">Semantic network</a></li> <li><a href="/wiki/Simple_Knowledge_Organization_System" title="Simple Knowledge Organization System">Simple Knowledge Organization System</a></li> <li><a href="/wiki/Speech_corpus" title="Speech corpus">Speech corpus</a></li> <li><a href="/wiki/Text_corpus" title="Text corpus">Text corpus</a></li> <li><a href="/wiki/Thesaurus_(information_retrieval)" title="Thesaurus (information retrieval)">Thesaurus (information retrieval)</a></li> <li><a href="/wiki/Treebank" title="Treebank">Treebank</a></li> <li><a href="/wiki/Universal_Dependencies" title="Universal Dependencies">Universal Dependencies</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Data</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/BabelNet" title="BabelNet">BabelNet</a></li> <li><a href="/wiki/Bank_of_English" title="Bank of English">Bank of English</a></li> <li><a href="/wiki/DBpedia" title="DBpedia">DBpedia</a></li> <li><a href="/wiki/FrameNet" title="FrameNet">FrameNet</a></li> <li><a href="/wiki/Google_Ngram_Viewer" class="mw-redirect" title="Google Ngram Viewer">Google Ngram Viewer</a></li> <li><a href="/wiki/UBY" title="UBY">UBY</a></li> <li><a href="/wiki/WordNet" title="WordNet">WordNet</a></li> <li><a href="/wiki/Wikidata" title="Wikidata">Wikidata</a></li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Automatic_identification_and_data_capture" title="Automatic identification and data capture">Automatic identification<br />and data capture</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Speech_recognition" title="Speech recognition">Speech recognition</a></li> <li><a href="/wiki/Speech_segmentation" title="Speech segmentation">Speech segmentation</a></li> <li><a href="/wiki/Speech_synthesis" title="Speech synthesis">Speech synthesis</a></li> <li><a class="mw-selflink selflink">Natural language generation</a></li> <li><a href="/wiki/Optical_character_recognition" title="Optical character recognition">Optical character recognition</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Topic_model" title="Topic model">Topic model</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Document_classification" title="Document classification">Document classification</a></li> <li><a href="/wiki/Latent_Dirichlet_allocation" title="Latent Dirichlet allocation">Latent Dirichlet allocation</a></li> <li><a href="/wiki/Pachinko_allocation" title="Pachinko allocation">Pachinko allocation</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Computer-assisted_reviewing" title="Computer-assisted reviewing">Computer-assisted<br />reviewing</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Automated_essay_scoring" title="Automated essay scoring">Automated essay scoring</a></li> <li><a href="/wiki/Concordancer" title="Concordancer">Concordancer</a></li> <li><a href="/wiki/Grammar_checker" title="Grammar checker">Grammar checker</a></li> <li><a href="/wiki/Predictive_text" title="Predictive text">Predictive text</a></li> <li><a href="/wiki/Pronunciation_assessment" title="Pronunciation assessment">Pronunciation assessment</a></li> <li><a href="/wiki/Spell_checker" title="Spell checker">Spell checker</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Natural-language_user_interface" title="Natural-language user interface">Natural language<br />user interface</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Chatbot" title="Chatbot">Chatbot</a></li> <li><a href="/wiki/Interactive_fiction" title="Interactive fiction">Interactive fiction</a> (c.f. <a href="/wiki/Syntax_guessing" class="mw-redirect" title="Syntax guessing">Syntax guessing</a>)</li> <li><a href="/wiki/Question_answering" title="Question answering">Question answering</a></li> <li><a href="/wiki/Virtual_assistant" title="Virtual assistant">Virtual assistant</a></li> <li><a href="/wiki/Voice_user_interface" title="Voice user interface">Voice user interface</a></li></ul> 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