GPSM: a Generaized Probabilistic Semantic Model for ambiguity resolution

Authors:
Jing-Shin Chang;Yih-Fen Luo;Keh-Yih Su
Affiliations:
National Tsing Hua University, Hsinchu, Taiwan, R.O.C.;Behavior Design Corporation, Hsinchu, Taiwan, R.O.C.;National Tsing Hua University, Hsinchu, Taiwan, R.O.C.
Venue:
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Year:
1992

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Abstract

In natural language processing, ambiguity resolution is a central issue, and can be regarded as a preference assignment problem. In this paper, a Generalized Probabilistic Semantic Model (GPSM) is proposed for preference computation. An effective semantic tagging procedure is proposed for tagging semantic features. A semantic score function is derived based on a score function, which integrates lexical, syntactic and semantic preference under a uniform formulation. The semantic score measure shows substantial improvement in structural disambiguation over a syntax-based approach.