2007 TopicsinSemanticRepresentation

Subject Headings: Latent Semantic Analysis.

Notes

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 TopicsinSemanticRepresentation	Thomas L. Griffiths Mark Steyvers Joshua B. Tenenbaum			Topics in Semantic Representation.				10.1037/0033-295X.114.2.211		2007