An Empirical Study on Thematic Knowledge Acquisition Based on Syntactic Clues and Heuristics

AN EMPIRICAL STUDY ON THEMATIC KNOWLEDGE ACQUISITION BASED ON SYNTACTIC CLUES AND HEURISTICS Rey-Long Liu* and Von-Wun Soo** Department of Computer Science National Tsing-Hua University HsinChu, Taiwan, R.O.C. Email: dr798303@cs.nthu.edu.tw* and soo@cs.nthu.edu.tw** Abstract Thematic knowledge is a basis of semamic interpreta- tion. In this paper, we propose an acquisition method to acquire thematic knowledge by exploiting syntactic clues from training sentences. The syntactic clues, which may be easily collected by most existing syn- tactic processors, reduce the hypothesis space of the thematic roles. The ambiguities may be further resolved by the evidences either from a trainer or from a large corpus. A set of heurist-cs based on linguistic constraints is employed to guide the ambi- guity resolution process. When a train,-.r is available, the system generates new sentences wtose thematic validities can be justified by the trainer. When a large corpus is available, the thematic validity may be justi- fied by observing the sentences in the corpus. Using this way, a syntactic processor may become a thematic recognizer by simply derivir.g its thematic knowledge from its own syntactic knowledge. Keywords: Thematic Knowledge Acquisition, Syntac- tic Clues, Heuristics-guided Ambigu-ty Resolution, Corpus-based Acquisition, Interactive Acquisition 1. INTRODUCTION Natural language processing (NLP) systems need various knowledge including syntactic, semantic, discourse, and pragmatic knowledge in different applications. Perhaps due to the relatively well- established syntactic theories and forrc.alisms, there were many syntactic processing systew, s either manu- ally constructed or automatically extenJ~d by various acquisition methods (Asker92, Berwick85, Brentgl, Liu92b, Lytinen90, Samuelsson91, Simmons91 Sanfi- lippo92, Smadja91 and Sekine92). However, the satis- factory representation and acquisition methods of domain-independent semantic, disco~lrse, and prag- matic knowledge are not yet develo~d or computa- tionally implemented. NLP systems 6f'.en suffer the dilemma of semantic representation. Sophisticated representation of semantics has better expressive power but imposes difficulties on acquF;ition in prac- tice. On the other hand, the poor adequacy of naive semantic representation may deteriorate the perfor- mance of NLP systems. Therefore, for plausible acquisition and processing, domain-dependent seman- tic bias was 9ften employed in many previous acquisi- tion systez, s (Grishman92b, Lang88, Lu89, and Velardi91). In thi~ paper, we present an implemented sys- tem that acquires domain-independent thematic knowledge using available syntactic resources (e.g. syntactic p~acessing systems and syntactically pro- cessed cort;ara). Thematic knowledge can represent semantic or conceptual entities. For correct and effi- cient parsing, thematic expectation serves as a basis for conflict resolution (Taraban88). For natural language understanding and other applications (e.g. machine translation), thematic role recognition is a major step. ~ematic relations may serve as the voca- bulary shared by the parser, the discourse model, and the world knowledge (Tanenhaus89). More impor- tantly, since thematic structures are perhaps most closely link~d to syntactic structures ($ackendoff72), thematic knowledge acquisition may be more feasible when only .:'yntactic resources are available. The con- sideration of the availability of the resources from which thematic knowledge may be derived promotes the practica2 feasibility of the acquisition method. In geaeral, lexical knowledge of a lexical head should (at ~east) include 1) …

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