An introduction to latent semantic analysis

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given...

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Vydané v:	Discourse processes Ročník 25; číslo 2-3; s. 259 - 284
Hlavní autori:	Landauer, Thomas K, Foltz, Peter W., Laham, Darrell
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Taylor & Francis Group 01.01.1998
Predmet:	Cognitive Processes Coherence Computer Generated Language Analysis Context Discourse Analysis Information Content Knowledge Representation Language Research Latent Semantic Analysis Research Methodology Semantic Analysis Semantics Statistical Analysis Text Analysis Word Meaning
ISSN:	0163-853X, 1532-6950
On-line prístup:	Získať plný text
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Abstract	Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word-word and passage-word lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.
AbstractList	Latent semantic analysis is a theory & method for extracting & representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer, & Dumais, S. T., 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does & does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words & sets of words to each other. The adequacy of latent semantic analysis reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary & subject matter tests; it mimics human word sorting & category judgments; it simulates word-word & passage-word lexical priming data; & it accurately estimates passage coherence, learnability of passages by individual students, & the quality & quantity of knowledge contained in an essay. 1 Table, 5 Figures, 1 Appendix, 35 References. Adapted from the source document Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word-word and passage-word lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay. Offers an introduction to the theory and implementation of Latent Semantic Analysis (LSA), a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. Gives an overview of applications and modeling of human knowledge to which LSA has been applied. (SR)
Author	Laham, Darrell Foltz, Peter W. Landauer, Thomas K
Author_xml	– sequence: 1 givenname: Thomas K surname: Landauer fullname: Landauer, Thomas K organization: Department of Psychology , University of Colorado – sequence: 2 givenname: Peter W. surname: Foltz fullname: Foltz, Peter W. organization: Department of Psychology , New Mexico State University – sequence: 3 givenname: Darrell surname: Laham fullname: Laham, Darrell organization: Department of Psychology , University of Colorado
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Cites_doi	10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 10.1080/01638539809545027 10.1207/s1532690xci1401_1 10.1145/138859.138866 10.1037/0003-066X.49.4.294 10.1080/01638539809545030 10.3758/BF03204766 10.1080/01638539809545031 10.3758/BF03197039 10.1037/0033-295X.104.2.211 10.1080/01638539809545029 10.1037/0033-295X.95.2.163 10.1177/109434209200600103 10.1137/1037127 10.3758/BF03204765 10.1080/01638539809545025
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Snippet	Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied... Offers an introduction to the theory and implementation of Latent Semantic Analysis (LSA), a theory and method for extracting and representing the... Latent semantic analysis is a theory & method for extracting & representing the contextual-usage meaning of words by statistical computations applied to a...
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SubjectTerms	Cognitive Processes Coherence Computer Generated Language Analysis Context Discourse Analysis Information Content Knowledge Representation Language Research Latent Semantic Analysis Research Methodology Semantic Analysis Semantics Statistical Analysis Text Analysis Word Meaning
Title	An introduction to latent semantic analysis
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