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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Discourse processes Ročník 25; číslo 2-3; s. 259 - 284
Hlavní autoři: Landauer, Thomas K, Foltz, Peter W., Laham, Darrell
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis Group 01.01.1998
Témata:
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 přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0163-853X
1532-6950
DOI:10.1080/01638539809545028