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ACL
1992

GPSM: A Generalized Probabilistic Semantic Model for Ambiguity Resolution

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GPSM: A Generalized Probabilistic Semantic Model for Ambiguity Resolution
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.
Jing-Shin Chang, Yih-Fen Luo, Keh-Yih Su
Added 06 Nov 2010
Updated 06 Nov 2010
Type Conference
Year 1992
Where ACL
Authors Jing-Shin Chang, Yih-Fen Luo, Keh-Yih Su
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