Abstract
Representations for semantic information about words are necessary for many applications of neural networks in natural language processing. This paper describes an efficient, corpus-based method for inducing distributed semantic representations for a large number of words (50,000) from lexical coccurrence statistics by means of a large-scale linear regression. The representations are successfully applied to word sense disambiguation using a nearest neighbor method.
Keywords
Computer scienceNatural language processingArtificial intelligenceWord (group theory)Word-sense disambiguationSemantic compressionSemantic spaceSemantics (computer science)Natural languageArtificial neural networkSemEvalSpace (punctuation)Semantic computingWordNetLinguisticsSemantic Web
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Publication Info
- Year
- 1992
- Type
- article
- Volume
- 5
- Pages
- 895-902
- Citations
- 212
- Access
- Closed
External Links
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212
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Cite This
Hinrich Schütze
(1992).
Word Space.
Neural Information Processing Systems
, 5
, 895-902.