Guiding an HPSG Parser using Semantic and Pragmatic Expectations

Guiding an HPSG Parser using Semantic and Pragmatic Expectations Jim Skon Computer and Information Science Department The Ohio State University Columbus, OH 43210, USA Internet: skon@ cis.ohio-state.edu Abstract 1 Efficient natural language generation has been successfully demonstrated using highly compiled knowledge about speech acts and their related social actions. A design and prototype implementation of a parser which utilizes this same pragmatic knowledge to efficiently guide parsing is presented. Such guidance is shown to prune the search space and thus avoid needless processing of pragmatically unlikely constituent structures. INTRODUCTION The use of purely syntactic knowledge during the parse phase of natural language understanding yields considerable local ambiguity (consideration of impossible subeonstituents) as well global ambiguity (construction of syntactically valid parses not applicable to the socio-pragmatic context). This research investigates bringing socio-pragmatic knowledge to bear during the parse, while maintaining a domain independent grammar and parser. The particular technique explored uses knowledge about the pragmatic context to order the consideration of proposed parse constituents, thus guiding the parser to consider the best (wrt the expectations) solutions first. Such a search may be classified as a best- first search. The theoretical models used to represent the pragmatic knowledge in this study are based on Halliday's Systemic Grammar and a model of the pragmatics of conversation. The model used to represent the syntax and domain independent semantic knowledge is HPSG - Head-driven Phrase Structure Grammar. BACKGROUND Patten, Geis and Becker (1992) demonstrate the application of knowledge compilation to achieve the rapid generation of natural language. Their mechanism is based on Halliday's systemic networks, and on Geis' theory of the pragmatics of conversation. A model of conversation using principled compilation of pragmatic knowledge and other linguistic knowledge is used to permit the application of pragmatic inference without expensive computation. A pragmatic component is used to model social action, including speech acts, and utilize conventions of us.g involving such features of context such as politeness, ~e~gister, and stylistic features. These politeness features are critiqd}l to the account of indirect speech acts. This pragmatic knovCledge is compiled into course-grained knowledge in the form of a classification hierarchy. A planner component uses knowledge about conditions which need to be satisfied (discourse goals) to produce a set of pragmatic features which characterize a desired utterance. These features are mapped into the systemic l Research Funded by The Ohio State Center for Cognitive Science and The Ohio State Departments of Computer and Information Science and Linguistics grammar (using compiled knowledge) which is then used to realize the actual utterance. The syntactic/semantic component used in this study is a parser based on the HPSG (Head Driven Phrase Structure Grammar) theory of grammar (Pollard and Sag, 1992). HPSG models all linguistic constituents in terms of part/a/ information structures called feature structures. Linguistic signs incorporate simultaneous representation of phonological, syntactic, and semantic attributes of grammatical constituents. HPSG is a lexiealized theory, with the lexical definitions, rather then phrase structure rules, specifying most configurational constraints. Control (such as subcategorization, for example) is asserted …

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