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<div id="contentSub"><div id="mw-content-subtitle"></div></div> <div id="mw-content-text" class="mw-body-content"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><p><b>Never-Ending Language Learning</b> system (<b>NELL</b>) is a <a href="/wiki/Semantics" title="Semantics">semantic</a> <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a> <a href="/wiki/Computer_system" class="mw-redirect" title="Computer system">system</a> that as of 2010 was being developed by a research team at <a href="/wiki/Carnegie_Mellon_University" title="Carnegie Mellon University">Carnegie Mellon University</a>, and supported by grants from <a href="/wiki/DARPA" title="DARPA">DARPA</a>, <a href="/wiki/Google" title="Google">Google</a>, <a href="/wiki/National_Science_Foundation" title="National Science Foundation">NSF</a>, and <a href="/wiki/CNPq" class="mw-redirect" title="CNPq">CNPq</a> with portions of the system running on a <a href="/wiki/Supercomputer" title="Supercomputer">supercomputing</a> <a href="/wiki/Computer_cluster" title="Computer cluster">cluster</a> provided by <a href="/wiki/Yahoo!" class="mw-redirect" title="Yahoo!">Yahoo!</a>.<sup id="cite_ref-NYT2010_1-0" class="reference"><a href="#cite_note-NYT2010-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Process_and_goals">Process and goals</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Never-Ending_Language_Learning&action=edit&section=1" title="Edit section: Process and goals"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1251242444">.mw-parser-output .ambox{border:1px solid #a2a9b1;border-left:10px solid #36c;background-color:#fbfbfb;box-sizing:border-box}.mw-parser-output .ambox+link+.ambox,.mw-parser-output .ambox+link+style+.ambox,.mw-parser-output .ambox+link+link+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+style+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+link+.ambox{margin-top:-1px}html body.mediawiki .mw-parser-output .ambox.mbox-small-left{margin:4px 1em 4px 0;overflow:hidden;width:238px;border-collapse:collapse;font-size:88%;line-height:1.25em}.mw-parser-output .ambox-speedy{border-left:10px solid #b32424;background-color:#fee7e6}.mw-parser-output .ambox-delete{border-left:10px solid #b32424}.mw-parser-output .ambox-content{border-left:10px solid #f28500}.mw-parser-output .ambox-style{border-left:10px solid #fc3}.mw-parser-output .ambox-move{border-left:10px solid #9932cc}.mw-parser-output .ambox-protection{border-left:10px solid #a2a9b1}.mw-parser-output .ambox .mbox-text{border:none;padding:0.25em 0.5em;width:100%}.mw-parser-output .ambox .mbox-image{border:none;padding:2px 0 2px 0.5em;text-align:center}.mw-parser-output .ambox .mbox-imageright{border:none;padding:2px 0.5em 2px 0;text-align:center}.mw-parser-output .ambox .mbox-empty-cell{border:none;padding:0;width:1px}.mw-parser-output .ambox .mbox-image-div{width:52px}@media(min-width:720px){.mw-parser-output .ambox{margin:0 10%}}@media print{body.ns-0 .mw-parser-output .ambox{display:none!important}}</style><table class="box-Update plainlinks metadata ambox ambox-content ambox-Update" role="presentation"><tbody><tr><td class="mbox-image"><div class="mbox-image-div"><span typeof="mw:File"><span><img alt="" src="//upload.wikimedia.org/wikipedia/commons/thumb/5/53/Ambox_current_red_Americas.svg/42px-Ambox_current_red_Americas.svg.png" decoding="async" width="42" height="34" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/5/53/Ambox_current_red_Americas.svg/63px-Ambox_current_red_Americas.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/53/Ambox_current_red_Americas.svg/84px-Ambox_current_red_Americas.svg.png 2x" data-file-width="360" data-file-height="290" /></span></span></div></td><td class="mbox-text"><div class="mbox-text-span">This section needs to be <b>updated</b>.<span class="hide-when-compact"> Please help update this article to reflect recent events or newly available information.</span> <span class="date-container"><i>(<span class="date">October 2023</span>)</i></span></div></td></tr></tbody></table> <p>NELL was programmed by its developers to be able to identify a basic set of fundamental semantic relationships between a few hundred predefined categories of data, such as cities, companies, emotions and sports teams. Since the beginning of 2010, the Carnegie Mellon research team has been running NELL around the clock, sifting through hundreds of millions of web pages looking for connections between the information it already knows and what it finds through its search process – to make new connections in a manner that is intended to mimic the way humans learn new information.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> For example, in encountering the word pair "Pikes Peak", NELL would notice that both words are capitalized and deduce from the second word that it was the name of a mountain, and then build on the relationship of words surrounding those two words to deduce other connections.<sup id="cite_ref-NYT2010_1-1" class="reference"><a href="#cite_note-NYT2010-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> </p><p>The goal of NELL and other semantic learning systems, such as <a href="/wiki/IBM" title="IBM">IBM</a>'s <a href="/wiki/Watson_(artificial_intelligence_software)" class="mw-redirect" title="Watson (artificial intelligence software)">Watson</a> system, is to be able to develop means of <a href="/wiki/Question_answering" title="Question answering">answering questions</a> posed by users in natural language with no human intervention in the process.<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Oren_Etzioni" title="Oren Etzioni">Oren Etzioni</a> of the <a href="/wiki/University_of_Washington" title="University of Washington">University of Washington</a> lauded the system's "continuous learning, as if NELL is exercising curiosity on its own, with little human help".<sup id="cite_ref-NYT2010_1-2" class="reference"><a href="#cite_note-NYT2010-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> </p><p>By October 2010, NELL has doubled the number of relationships it has available in its knowledge base and has learned 440,000 new facts, with an accuracy of 87%.<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-NYT2010_1-3" class="reference"><a href="#cite_note-NYT2010-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> Team leader <a href="/wiki/Tom_M._Mitchell" title="Tom M. Mitchell">Tom M. Mitchell</a>, chairman of the machine learning department at Carnegie Mellon described how NELL "self-corrects when it has more information, as it learns more", though it does sometimes arrive at incorrect conclusions. Accumulated errors, such as the deduction that <a href="/wiki/HTTP_cookie" title="HTTP cookie">Internet cookies</a> were a kind of baked good, led NELL to deduce from the phrases "I deleted my Internet cookies" and "I deleted my files" that "<a href="/wiki/Computer_file" title="Computer file">computer files</a>" also belonged in the baked goods category.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> Clear errors like these are<sup class="noprint Inline-Template" style="white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Manual_of_Style/Dates_and_numbers#Chronological_items" title="Wikipedia:Manual of Style/Dates and numbers"><span title="The time period mentioned near this tag is ambiguous. (September 2023)">when?</span></a></i>]</sup> corrected every few weeks by the members of the research team and the system is allowed to continue its learning process.<sup id="cite_ref-NYT2010_1-4" class="reference"><a href="#cite_note-NYT2010-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> By 2018, NELL had "acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., <i>servedWith(tea,biscuits)</i>), while learning thousands of interrelated functions that continually improve its reading competence over time."<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><p>As of September 2023, the project's most recently gathered facts dated from February 2019 (according to its Twitter feed)<sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> or September 2018 (according to its home page).<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> <div class="mw-heading mw-heading2"><h2 id="Reception">Reception</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Never-Ending_Language_Learning&action=edit&section=2" title="Edit section: Reception"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In his 2019 book "<a href="/wiki/Human_Compatible" title="Human Compatible">Human Compatible</a>", <a href="/wiki/Stuart_J._Russell" title="Stuart J. Russell">Stuart Russell</a> commented that 'Unfortunately NELL has confidence in only 3 percent of its beliefs and relies on human experts to clean out false or meaningless beliefs on a regular basis—such as its beliefs that “Nepal is a country also known as United States” and "value is an agricultural product that is usually cut into basis."'<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> A 2023 paper commented that "While the <i>never-ending</i> part seems like the right approach, NELL still had the drawback that its focus remained much too grounded on object-language descriptions, and relied on web pages as its only source, which significantly influenced the type of grammar, symbolism, slang, etc. analysed."<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> </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=Never-Ending_Language_Learning&action=edit&section=3" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/Cognitive_architecture" title="Cognitive architecture">Cognitive architecture</a></li> <li><a href="/wiki/Computational_models_of_language_acquisition" class="mw-redirect" title="Computational models of language acquisition">Computational models of language acquisition</a></li> <li><a href="/wiki/Cyc" title="Cyc">Cyc</a></li> <li><a href="/wiki/Darwin_among_the_Machines" title="Darwin among the Machines">Darwin among the Machines</a></li> <li><a href="/wiki/The_Adolescence_of_P-1" title="The Adolescence of P-1">The Adolescence of P-1</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=Never-Ending_Language_Learning&action=edit&section=4" 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 .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output 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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 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