An Introduction to Latent Semantic Analysis Export

@article{LandauerEtAl1998:IntroToLSA, 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 and 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.}, author = {Landauer, Thomas K. and Foltz, Peter W. and Laham, Darrell}, citeulike-article-id = {1152071}, comment = {[Landauer et al., 1998] Landauer TK, Foltz PW and Laham D, An Introduction to Latent Semantic Analysis, Discourse Processes, p. 259, 1998.}, journal = {Discourse Processes}, keywords = {concept\_extraction, document\_clustering, document\_linking, information\_extraction, information\_retrieval, lsa}, pages = {259--284}, posted-at = {2007-03-09 23:22:24}, priority = {0}, title = {An Introduction to Latent Semantic Analysis}, volume = {25}, year = {1998} }

TY - JOUR ID - LandauerEtAl1998:IntroToLSA L3 - citeulike-article-id:1152071 N2 - 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 and 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. JF - Discourse Processes EP - 284 TI - An Introduction to Latent Semantic Analysis VL - 25 SP - 259 KW - concept_extraction KW - document_clustering KW - document_linking KW - information_extraction KW - information_retrieval KW - lsa N1 - [Landauer et al., 1998] Landauer TK, Foltz PW and Laham D, An Introduction to Latent Semantic Analysis, Discourse Processes, p. 259, 1998. AU - Landauer, Thomas AU - Foltz, Peter AU - Laham, Darrell PY - 1998/// ER -