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.

• Type: Conference
• Year: 1992
• Where: ACL
• Authors: Jing-Shin Chang, Yih-Fen Luo, Keh-Yih Su
• Keywords: ACL 1992, ACL 2007, Semantic, Semantic Score, Semantic Tagging Procedure