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  • × author_ss:"Landauer, T.K."
  1. Furnas, G.W.; Landauer, T.K.: Describing categories of objects for menu retrieval systems (1984) 0.00 
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  2. Landauer, T.K.; Foltz, P.W.; Laham, D.: ¬An introduction to Latent Semantic Analysis (1998) 0.00 
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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 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.
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  3. Furnas, G.W.; Landauer, T.K.; Gomez, L.M.; Dumais, S.T.: ¬The vocabulary problem in human-system communication (1987) 0.00 
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  4. Gomez, L.; Lochbaum, C.C.; Landauer, T.K.: All the right words: finding what you want as an function of richness of indexing vocabulary (1990) 0.00 
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  5. Deerwester, S.C.; Dumais, S.T.; Landauer, T.K.; Furnas, G.W.; Harshman, R.A.: Indexing by latent semantic analysis (1990) 0.00 
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    Abstract
    A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents ("semantic structure") in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. Initial tests find this completely automatic method for retrieval to be promising.
    Type
    a

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