GPSM: A Generalized Probabilistic Semantic Model for Ambiguity Resolution

GPSM: A GENERALIZED PROBABILISTIC SEMANTIC MODEL FOR AMBIGUITY RESOLUTION tJing-Shin Chang, *Yih-Fen Luo and tKeh-Yih Su tDepartment of Electrical Engineering National Tsing Hua University Hsinchu, TAIWAN 30043, R.O.C. tEmail: shin@ee.nthu.edu.tw, kysu@ee.nthu.edu.tw *Behavior Design Corporation No. 28, 2F, R&D Road II, Science-Based Industrial Park Hsinchu, TAIWAN 30077, R.O.C. ABSTRACT 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. 1. Introduction In a large natural language processing system, such as a machine translation system (MTS), am- biguity 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 re- solving ambiguity. Any parse that violates the se- mantic constraints is regarded as ungrammatical and rejected. Unfortunately, because every "rule" tends to have exception and uncertainty, and ill- formedness has significant contribution to the er- ror rate of a large practical system, such "hard rejection" approaches fail to deal with these situa- tions. A better way is to find all possible interpre- tations and place emphases on preference, rather than weU-formedness (e.g., [Wilks 83].) However, most of the known approaches for giving prefer- ence depend heavily on heuristics such as counting the number of constraint satisfactions. Therefore, most such preference measures can not be objec- tively justified. Moreover, it is hard and cosily to acquire, verify and maintain the consistency of the large fine-grained rule base by hand. Probabilistic approaches greatly relieve the knowledge acquisition problem because they are usually trainable, consistent and easy to meet cer- tain optimum criteria. They can also provide more objective preference measures for "soft re- jection." Hence, they are attractive for a large sys- tem. The current probabilistic approaches have a wide coverage including lexical analysis [DeRose 88, Church 88], syntactic analysis [Garside 87, Fujisaki 89, Su 88, 89, 91b], restricted semantic analysis [Church 89, Liu 89, 90], and experimental translation systems [Brown 90]. However, there is still no integrated approach for modeling the joint effects of lexical, syntactic and semantic in- formation on preference evaluation. A generalized probabilistic semantic model (GPSM) will be proposed in this paper to over- come the above problems. In particular, an in- tegrated formulation for lexical, syntactic and se- mantic knowledge will be used to derive the se- mantic score for semantic preference evaluation. Application of the model to structural disam- 177 biguation is investigated. Preliminary experiments show about 10%-14% improvement of the seman- tic score measure over a model that uses syntactic information only. 2. …

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