@article{landauer98introduction,
title = {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},
description = {CiteULike import},
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.},
priority = {1}, citeulike-article-id = {1044611},
keywords = {language lsa lsi }
}
::