tailieunhanh - Báo cáo khoa học: "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. . | GPSM A GENERALIZED PROBABILISTIC SEMANTIC MODEL FOR AMBIGUITY RESOLUTION tjing-Shin Chang Yih-Fen Luo and tKeh-Yih Su Department of Electrical Engineering National Tsing Hua University Hsinchu TAIWAN 30043 . tEmail shin@ kysu@ Behavior Design Corporation No. 28 2F R D Road II Science-Based Industrial Park Hsinchu TAIWAN 30077 . 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. 1. Introduction In a large natural language processing system such as a machine translation system MTS ambiguity resolution is a critical problem. Various rule-based and probabilistic approaches had been proposed to resolve various kinds of ambiguity problems on a case-by-case basis. In rule-based systems a large number of rules are used to specify linguistic constraints for resolving ambiguity. Any parse that violates the semantic constraints is regarded as ungrammatical and rejected. Unfortunately because every rule tends to have exception and uncertainty and informedness has significant contribution to the error rate of a large practical system such hard rejection approaches fail to deal with these situations. A better way is to find all possible interpretations and place emphases on preference rather than well-formedness . Wilks 83 . However most of the known approaches for giving preference depend heavily on heuristics such as counting the number of constraint satisfactions. Therefore most such preference measures can .