Paper: GPSM: A Generalized Probabilistic Semantic Model For Ambiguity Resolution

ACL ID P92-1023
Title GPSM: A Generalized Probabilistic Semantic Model For Ambiguity Resolution
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1992
Authors

In natural language processing, ambiguity res- olution 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 de- rived based on a score function, which inte- grates lexical, syntactic and semantic prefer- ence under a uniform formulation. The se- mantic score measure shows substantial im- provement in structural disambiguation over a syntax-based approach.