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39
Indexing by latent semantic analysis
- JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
, 1990
"... 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 p ..."
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Cited by 2168 (30 self)
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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 or-thogonal 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 re-turned. initial tests find this completely automatic method for retrieval to be promising.
A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
- Psychological review
, 1997
"... How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LS ..."
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Cited by 764 (9 self)
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How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched. Prologue "How much do we know at any time? Much more, or so I believe, than we know we know!" —Agatha Christie, The Moving Finger A typical American seventh grader knows the meaning of
Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora
, 1992
"... This paper describes a program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roget's index tend to correspond to ..."
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Cited by 265 (10 self)
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This paper describes a program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roget's index tend to correspond to sense distinctions; thus selecting the most likely category provides a useful level of sense disambiguation. The selection of categories is accomplished by identifying and weighting words that are indicative of each category when seen in context, using a Bayesian theoretical framework. Other
Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
- IN PROCEEDINGS OF THE 34TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
, 1996
"... In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach ..."
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Cited by 204 (7 self)
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In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach
Introduction to the special issue on word sense disambiguation
- Computational Linguistics J
, 1998
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Extended gloss overlaps as a measure of semantic relatedness
- In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence
, 2003
"... This paper presents a new measure of semantic relatedness between concepts that is based on the number of shared words (overlaps) in their definitions (glosses). This measure is unique in that it extends the glosses of the concepts under consideration to include the glosses of other concepts to whic ..."
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Cited by 121 (5 self)
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This paper presents a new measure of semantic relatedness between concepts that is based on the number of shared words (overlaps) in their definitions (glosses). This measure is unique in that it extends the glosses of the concepts under consideration to include the glosses of other concepts to which they are related according to a given concept hierarchy. We show that this new measure reasonably correlates to human judgments. We introduce a new method of word sense disambiguation based on extended gloss overlaps, and demonstrate that it fares well on the SENSEVAL-2 lexical sample data. 1
Lexical Ambiguity and Information Retrieval
- ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 2000
"... Lexical ambiguity is a pervasive problem in natural language processing. However, little quantitative information is available about the extent of the problem, or about the impact that it has on information retrieval systems. We report on an analysis of lexical ambiguity in information retrieval ..."
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Cited by 113 (3 self)
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Lexical ambiguity is a pervasive problem in natural language processing. However, little quantitative information is available about the extent of the problem, or about the impact that it has on information retrieval systems. We report on an analysis of lexical ambiguity in information retrieval test collections, and on experiments to determine the utility of word meanings for separating relevant from non-relevant documents. The experiments show that there is considerable ambiguity even in a specialized database. Word senses
Word sense disambiguation: The state of the art
- Computational Linguistics
, 1998
"... The automatic disambiguation of word senses has been an interest and concern since the earliest days of computer treatment of language in the 1950's. Sense disambiguation is an “intermediate task ” (Wilks and Stevenson, 1996) which is not an end in itself, but rather is necessary at one level or ano ..."
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Cited by 92 (3 self)
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The automatic disambiguation of word senses has been an interest and concern since the earliest days of computer treatment of language in the 1950's. Sense disambiguation is an “intermediate task ” (Wilks and Stevenson, 1996) which is not an end in itself, but rather is necessary at one level or another to accomplish most natural language processing tasks. It is
Part-of-Speech Tagging and Partial Parsing
- Corpus-Based Methods in Language and Speech
, 1996
"... m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the va ..."
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Cited by 85 (0 self)
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m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on
An adapted lesk algorithm for word sense disambiguation using wordnet
- In Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics
, 2002
"... This is to certify that I have examined this copy of master’s thesis by ..."
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Cited by 73 (2 self)
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This is to certify that I have examined this copy of master’s thesis by

