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width="50" height="39" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/9/99/Question_book-new.svg/75px-Question_book-new.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/9/99/Question_book-new.svg/100px-Question_book-new.svg.png 2x" data-file-width="512" data-file-height="399" /></a></span></div></td><td class="mbox-text"><div class="mbox-text-span">This article <b>needs additional citations for <a href="/wiki/Wikipedia:Verifiability" title="Wikipedia:Verifiability">verification</a></b>.<span class="hide-when-compact"> Please help <a href="/wiki/Special:EditPage/Document-term_matrix" title="Special:EditPage/Document-term matrix">improve this article</a> by <a href="/wiki/Help:Referencing_for_beginners" title="Help:Referencing for beginners">adding citations to reliable sources</a>. Unsourced material may be challenged and removed.<br /><small><span class="plainlinks"><i>Find sources:</i> <a rel="nofollow" class="external text" href="https://www.google.com/search?as_eq=wikipedia&q=%22Document-term+matrix%22">"Document-term matrix"</a> – <a rel="nofollow" class="external text" href="https://www.google.com/search?tbm=nws&q=%22Document-term+matrix%22+-wikipedia&tbs=ar:1">news</a> <b>·</b> <a rel="nofollow" class="external text" href="https://www.google.com/search?&q=%22Document-term+matrix%22&tbs=bkt:s&tbm=bks">newspapers</a> <b>·</b> <a rel="nofollow" class="external text" href="https://www.google.com/search?tbs=bks:1&q=%22Document-term+matrix%22+-wikipedia">books</a> <b>·</b> <a rel="nofollow" class="external text" href="https://scholar.google.com/scholar?q=%22Document-term+matrix%22">scholar</a> <b>·</b> <a rel="nofollow" class="external text" href="https://www.jstor.org/action/doBasicSearch?Query=%22Document-term+matrix%22&acc=on&wc=on">JSTOR</a></span></small></span> <span class="date-container"><i>(<span class="date">January 2021</span>)</i></span><span class="hide-when-compact"><i> (<small><a href="/wiki/Help:Maintenance_template_removal" title="Help:Maintenance template removal">Learn how and when to remove this message</a></small>)</i></span></div></td></tr></tbody></table> <p>A <b>document-term matrix</b> is a mathematical <a href="/wiki/Matrix_(mathematics)" title="Matrix (mathematics)">matrix</a> that describes the frequency of terms that occur in each document in a collection. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of a <b>document-feature matrix</b> where "features" may refer to other properties of a document besides terms.<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> It is also common to encounter the transpose, or <b>term-document matrix</b> where documents are the columns and terms are the rows. They are useful in the field of <a href="/wiki/Natural_language_processing" title="Natural language processing">natural language processing</a> and <a href="/w/index.php?title=Computational_text_analysis&action=edit&redlink=1" class="new" title="Computational text analysis (page does not exist)">computational text analysis</a>.<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> </p><p>While the value of the cells is commonly the raw count of a given term, there are various schemes for weighting the raw counts such as row normalizing (i.e. relative frequency/proportions) and <a href="/wiki/Tf-idf" class="mw-redirect" title="Tf-idf">tf-idf</a>. </p><p>Terms are commonly single words separated by whitespace or punctuation on either side (a.k.a. unigrams). In such a case, this is also referred to as "bag of words" representation because the counts of individual words is retained, but not the order of the words in the document. </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="General_concept">General concept</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Document-term_matrix&action=edit&section=1" title="Edit section: General concept"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>When creating a data-set of <a href="/wiki/Term_(language)" class="mw-redirect" title="Term (language)">terms</a> that appear in a corpus of <a href="/wiki/Document" title="Document">documents</a>, the document-term matrix contains rows corresponding to the documents and columns corresponding to the terms. Each <i>ij</i> cell, then, is the number of times word <i>j</i> occurs in document <i>i</i>. As such, each row is a vector of term counts that represents the content of the document corresponding to that row. For instance if one has the following two (short) documents: </p> <ul><li>D1 = "I like databases"</li> <li>D2 = "I dislike databases",</li></ul> <p>then the document-term matrix would be: </p> <table border="1" cellspacing="0"> <tbody><tr> <th></th> <th>I</th> <th>like</th> <th>dislike</th> <th>databases </th></tr> <tr align="center"> <td><b>D1</b></td> <td>1</td> <td>1</td> <td>0</td> <td>1 </td></tr> <tr align="center"> <td><b>D2</b></td> <td>1</td> <td>0</td> <td>1</td> <td>1 </td></tr></tbody></table> <p>which shows which documents contain which terms and how many times they appear. Note that, unlike representing a document as just a token-count list, the document-term matrix includes all terms in the corpus (i.e. the corpus vocabulary), which is why there are zero-counts for terms in the corpus which do not also occur in a specific document. For this reason, document-term matrices are usually stored in a sparse matrix format. </p><p>As a result of the power-law distribution of tokens in nearly every corpus (see <a href="/wiki/Zipf%27s_law" title="Zipf's law">Zipf's law</a>), it is common to weight the counts. This can be as simple as dividing counts by the total number of tokens in a document (called relative frequency or proportions), dividing by the maximum frequency in each document (called prop max), or taking the log of frequencies (called log count). If one desires to weight the words most unique to an individual document as compared to the corpus as a whole, it is common to use <a href="/wiki/Tf-idf" class="mw-redirect" title="Tf-idf">tf-idf</a>, which divides the term frequency by the term's document frequency. </p> <div class="mw-heading mw-heading2"><h2 id="History_of_the_concept">History of the concept</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Document-term_matrix&action=edit&section=2" title="Edit section: History of the concept"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The document-term matrix emerged in the earliest years of the computerization of text. The increasing capacity for storing documents created the problem of retrieving a given document in an efficient manner. While previously the work of classifying and indexing was accomplished by hand, researchers explored the possibility of doing this automatically using word frequency information. </p><p> One of the first published document-term matrices was in <a href="/w/index.php?title=Harold_Borko&action=edit&redlink=1" class="new" title="Harold Borko (page does not exist)">Harold Borko</a>'s 1962 article "The construction of an empirically based mathematically derived classification system" (page 282, see also his 1965 article<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>). Borko references two computer programs, "FEAT" which stood for "Frequency of Every Allowable Term," written by John C. Olney of the System Development Corporation and the Descriptor Word Index Program, written by <a href="/w/index.php?title=Eileen_Stone&action=edit&redlink=1" class="new" title="Eileen Stone (page does not exist)">Eileen Stone</a> also of the System Development Corporation: </p><blockquote><p>Having selected the documents which were to make up the experimental library, the next step consisted of keypunching the entire body of text preparatory to computer processing.  The program used for this analysis was FEAT (Frequency of Every Allowable Term).  it was written by John C. Olney of the System Development Corporation and is designed to perform frequency and summary counts of individual words and of word pairs.  The  output of this program is an alphabetical listing, by frequency of occurrence, of all word types which appeared in the text.  Certain function words such as and, the,  at, a, etc., were placed in a "forbidden word list" table, and the frequency of these words was recorded  in a separate listing... A special computer program, called the Descriptor Word Index Program, was written to provide this information and to prepare a document-term matrix in a form suitable for in-put to the Factor Analysis Program. The Descriptor Word Index program was prepared by Eileen Stone of the System Development Corporation.<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></p></blockquote><p>Shortly thereafter, <a href="/wiki/Gerard_Salton" title="Gerard Salton">Gerard Salton</a> published "Some hierarchical models for automatic document retrieval" in 1963 which also included a visual depiction of a document-term matrix.<sup id="cite_ref-:0_5-0" class="reference"><a href="#cite_note-:0-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> Salton was at Harvard University at the time and his work was supported by the Air Force Cambridge Research Laboratories and Sylvania Electric Products, Inc. In this paper, Salton introduces the document-term matrix by comparison to a kind of term-context matrix used to measure similarities between words:</p><blockquote><p>If it is desired to generate document associations or document clusters instead of word associations, the same procedures can be used with slight modifications. Instead of starting with a word-sentence matrix <i>C</i>,... it is now convenient to construct a word-document matrix <i>F,</i> listing frequency of occurrence of word W<sub>i</sub> in Document D<sub>j</sub>... Document similarities can now be computed as before by comparing pairs of rows and by obtaining similarity coefficients based on the frequency of co-occurrences of the content words included in the given document. This procedure produces a document-document similarity matrix which can in turn be used for the generation of document clusters...<sup id="cite_ref-:0_5-1" class="reference"><a href="#cite_note-:0-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup></p></blockquote><p>In addition to Borko and Salton, in 1964, F.W. Lancaster published a comprehensive review of automated indexing and retrieval. While the work was published while he worked at the Herner and Company in Washington D.C., the paper was written while he was "employed in research work at Aslib, on the Aslib Cranfield Project."<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> Lancaster credits Borko with the document-term matrix:</p><blockquote><p>Harold Borko, of the System Development Corporation, has carried this operation a little further. A significant group of clue words is chosen from the vocabulary of an experimental collection. These are arranged in a document/term matrix to show the frequency of occurrence of each term in each document.... A correlation coefficient for each word pair is then computed, based on their co-occurrence in the document set. The resulting term/term matrix... is then factor analysed and a series of factors are isolated. These factors, when interpreted and named on the basis of the terms with high loadings which appear in each of the factors, become the classes of an empirical classification. The terms with high loadings in each factor are the clue words or predictors of the categories. </p></blockquote> <div class="mw-heading mw-heading2"><h2 id="Choice_of_terms">Choice of terms</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Document-term_matrix&action=edit&section=3" title="Edit section: Choice of terms"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>A point of view on the matrix is that each row represents a document. In the <a href="/wiki/Vector_space_model" title="Vector space model">vectorial semantic model</a>, which is normally the one used to compute a document-term matrix, the goal is to represent the topic of a document by the frequency of semantically significant terms. The terms are semantic units of the documents. It is often assumed, for <a href="/wiki/Indo-European_languages" title="Indo-European languages">Indo-European languages</a>, that nouns, verbs and adjectives are the more significant <a href="/wiki/Syntactic_category" title="Syntactic category">categories</a>, and that words from those categories should be kept as terms. Adding <a href="/wiki/Collocation" title="Collocation">collocation</a> as terms improves the quality of the vectors, especially when computing similarities between documents. </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=Document-term_matrix&action=edit&section=4" title="Edit section: Applications"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-heading mw-heading3"><h3 id="Improving_search_results">Improving search results</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Document-term_matrix&action=edit&section=5" title="Edit section: Improving search results"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Latent_semantic_analysis" title="Latent semantic analysis">Latent semantic analysis</a> (LSA, performing <a href="/wiki/Singular-value_decomposition" class="mw-redirect" title="Singular-value decomposition">singular-value decomposition</a> on the document-term matrix) can improve search results by <a href="/wiki/Word-sense_disambiguation" title="Word-sense disambiguation">disambiguating</a> <a href="/wiki/Polysemy" title="Polysemy">polysemous words</a> and searching for <a href="/wiki/Synonym" title="Synonym">synonyms</a> of the query. However, searching in the high-dimensional continuous space is much slower than searching the standard <a href="/wiki/Trie" title="Trie">trie</a> data structure of search engines. </p> <div class="mw-heading mw-heading3"><h3 id="Finding_topics">Finding topics</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Document-term_matrix&action=edit&section=6" title="Edit section: Finding topics"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><a href="/wiki/Multivariate_analysis" class="mw-redirect" title="Multivariate analysis">Multivariate analysis</a> of the document-term matrix can reveal topics/themes of the corpus. Specifically, <a href="/wiki/Latent_semantic_analysis" title="Latent semantic analysis">latent semantic analysis</a> and <a href="/wiki/Data_clustering" class="mw-redirect" title="Data clustering">data clustering</a> can be used, and, more recently, <a href="/wiki/Probabilistic_latent_semantic_analysis" title="Probabilistic latent semantic analysis">probabilistic latent semantic analysis</a> with its generalization <a href="/wiki/Latent_Dirichlet_allocation" title="Latent Dirichlet allocation">Latent Dirichlet allocation</a>, and <a href="/wiki/Non-negative_matrix_factorization" title="Non-negative matrix factorization">non-negative matrix factorization</a>, have been found to perform well for this task. </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=Document-term_matrix&action=edit&section=7" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/Bag_of_words_model" class="mw-redirect" title="Bag of words model">Bag of words model</a></li></ul> <div class="mw-heading mw-heading3"><h3 id="Implementations">Implementations</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Document-term_matrix&action=edit&section=8" title="Edit section: Implementations"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a rel="nofollow" class="external text" href="http://nlp.fi.muni.cz/projekty/gensim">Gensim</a>: Open source Python framework for Vector Space modelling. Contains memory-efficient algorithms for constructing term-document matrices from text plus common transformations (<a href="/wiki/Tf-idf" class="mw-redirect" title="Tf-idf">tf-idf</a>, <a href="/wiki/Latent_semantic_analysis" title="Latent semantic analysis">LSA</a>, <a href="/wiki/Latent_Dirichlet_allocation" title="Latent Dirichlet allocation">LDA</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=Document-term_matrix&action=edit&section=9" 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 .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist"> <div class="mw-references-wrap"><ol class="references"> <li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text"><style data-mw-deduplicate="TemplateStyles:r1238218222">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .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 .cs1-maint{color:#18911f}}</style><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://tutorials.quanteda.io/basic-operations/dfm/">"Document-feature matrix :: Tutorials for quanteda"</a>. <i>tutorials.quanteda.io</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2021-01-02</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=tutorials.quanteda.io&rft.atitle=Document-feature+matrix+%3A%3A+Tutorials+for+quanteda&rft_id=https%3A%2F%2Ftutorials.quanteda.io%2Fbasic-operations%2Fdfm%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADocument-term+matrix" class="Z3988"></span></span> </li> <li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://www.dustinstoltz.com/blog/2020/12/1/creating-document-term-matrix-comparison-in-r">"15 Ways to Create a Document-Term Matrix in R"</a>. <i>Dustin S. Stoltz</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2021-01-02</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=Dustin+S.+Stoltz&rft.atitle=15+Ways+to+Create+a+Document-Term+Matrix+in+R&rft_id=https%3A%2F%2Fwww.dustinstoltz.com%2Fblog%2F2020%2F12%2F1%2Fcreating-document-term-matrix-comparison-in-r&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADocument-term+matrix" class="Z3988"></span></span> </li> <li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBorko1965" class="citation journal cs1">Borko, Harold (1965). <a rel="nofollow" class="external text" href="https://dx.doi.org/10.2466/pms.1965.20.2.393">"A Factor Analytically Derived Classification System for Psychological Reports"</a>. <i>Perceptual and Motor Skills</i>. <b>20</b> (2): 393–406. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.2466%2Fpms.1965.20.2.393">10.2466/pms.1965.20.2.393</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0031-5125">0031-5125</a>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/14279310">14279310</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:34230652">34230652</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Perceptual+and+Motor+Skills&rft.atitle=A+Factor+Analytically+Derived+Classification+System+for+Psychological+Reports&rft.volume=20&rft.issue=2&rft.pages=393-406&rft.date=1965&rft.issn=0031-5125&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A34230652%23id-name%3DS2CID&rft_id=info%3Apmid%2F14279310&rft_id=info%3Adoi%2F10.2466%2Fpms.1965.20.2.393&rft.aulast=Borko&rft.aufirst=Harold&rft_id=http%3A%2F%2Fdx.doi.org%2F10.2466%2Fpms.1965.20.2.393&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADocument-term+matrix" class="Z3988"></span></span> </li> <li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBorko1962" class="citation book cs1">Borko, Harold (1962). "The construction of an empirically based mathematically derived classification system". <i>Proceedings of the May 1-3, 1962, spring joint computer conference on - AIEE-IRE '62 (Spring)</i>. New York, New York, USA: ACM Press. pp. 279–289. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1145%2F1460833.1460865">10.1145/1460833.1460865</a></span>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/9781450378758" title="Special:BookSources/9781450378758"><bdi>9781450378758</bdi></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:6483337">6483337</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=The+construction+of+an+empirically+based+mathematically+derived+classification+system&rft.btitle=Proceedings+of+the+May+1-3%2C+1962%2C+spring+joint+computer+conference+on+-+AIEE-IRE+%2762+%28Spring%29&rft.place=New+York%2C+New+York%2C+USA&rft.pages=279-289&rft.pub=ACM+Press&rft.date=1962&rft_id=https%3A%2F%2Fapi.semanticscholar.org%2FCorpusID%3A6483337%23id-name%3DS2CID&rft_id=info%3Adoi%2F10.1145%2F1460833.1460865&rft.isbn=9781450378758&rft.aulast=Borko&rft.aufirst=Harold&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADocument-term+matrix" class="Z3988"></span></span> </li> <li id="cite_note-:0-5"><span class="mw-cite-backlink">^ <a href="#cite_ref-:0_5-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:0_5-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFSalton1963" class="citation journal cs1">Salton, Gerard (July 1963). <a rel="nofollow" class="external text" href="https://dx.doi.org/10.1002/asi.5090140307">"Some hierarchical models for automatic document retrieval"</a>. <i>American Documentation</i>. <b>14</b> (3): 213–222. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1002%2Fasi.5090140307">10.1002/asi.5090140307</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0096-946X">0096-946X</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=American+Documentation&rft.atitle=Some+hierarchical+models+for+automatic+document+retrieval&rft.volume=14&rft.issue=3&rft.pages=213-222&rft.date=1963-07&rft_id=info%3Adoi%2F10.1002%2Fasi.5090140307&rft.issn=0096-946X&rft.aulast=Salton&rft.aufirst=Gerard&rft_id=http%3A%2F%2Fdx.doi.org%2F10.1002%2Fasi.5090140307&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADocument-term+matrix" class="Z3988"></span></span> </li> <li id="cite_note-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-6">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLANCASTER1964" class="citation journal cs1">LANCASTER, F.W. (1964-01-01). <a rel="nofollow" class="external text" href="https://doi.org/10.1108/eb049960">"MECHANIZED DOCUMENT CONTROL: A Review of Some Recent Research"</a>. <i>ASLIB Proceedings</i>. <b>16</b> (4): 132–152. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1108%2Feb049960">10.1108/eb049960</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0001-253X">0001-253X</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=ASLIB+Proceedings&rft.atitle=MECHANIZED+DOCUMENT+CONTROL%3A+A+Review+of+Some+Recent+Research&rft.volume=16&rft.issue=4&rft.pages=132-152&rft.date=1964-01-01&rft_id=info%3Adoi%2F10.1108%2Feb049960&rft.issn=0001-253X&rft.aulast=LANCASTER&rft.aufirst=F.W.&rft_id=https%3A%2F%2Fdoi.org%2F10.1108%2Feb049960&rfr_id=info%3Asid%2Fen.wikipedia.org%3ADocument-term+matrix" class="Z3988"></span></span> </li> </ol></div></div> <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 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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 class="mw-selflink selflink">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 href="/wiki/Natural_language_generation" title="Natural language generation">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> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Related</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/Formal_semantics_(natural_language)" title="Formal semantics (natural language)">Formal semantics</a></li> <li><a href="/wiki/Hallucination_(artificial_intelligence)" title="Hallucination (artificial intelligence)">Hallucination</a></li> <li><a href="/wiki/Natural_Language_Toolkit" title="Natural Language Toolkit">Natural Language Toolkit</a></li> <li><a href="/wiki/SpaCy" title="SpaCy">spaCy</a></li></ul> </div></td></tr></tbody></table></div>    </div><noscript><img src="https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1" alt="" width="1" height="1" style="border: none; position: absolute;"></noscript> <div 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