@article{landauer1998,
        title = {An Introduction to Latent Semantic Analysis},
        author = {Thomas K. Landauer and Peter W. Foltz and Darrell Laham},
        journal = {Discourse Processes},
        pages = {259--284},
        volume = 25,
        year = 1998,
        id = {1152071},
	priority = {0},
	description = {Dissertation},
	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.},
	biburl = {http://www.bibsonomy.org/bibtex/218b9289484633f9d267548939f7c1685/lm77},
	keywords = {information_extraction, document_linking, concept_extraction, document_clustering, lsa information_retrieval,}
}