Combination of Symbolic and Statistical Approaches for Grammatical Knowledge Acquisition

Combination of Symbolic and Statistical Approaches for Grammatical Knowledge Acquisition Masaki KIYONO* and Jun'ichi TSUJII Centre for Computational Linguistics University of Manchester Institute of Science and Technology PO Box 88, Manchester, M60 1QD, United Kingdom kiyono©ccl .umist. ac.uk, tsuj ii~ccl .umist. ac .uk Abstract The framework we adopted for customiz- ing linguistic knowledge to individual ap- plication domains is an integration of sym- bolic and statistical approaches. In or- der to acquire domain specific knowledge, we have previously proposed a rule-based mechanism to hypothesize missing knowl- edge from partial parsing results of unsuc- cessfully parsed sentences. In this paper, we focus on the statistical process which se- lects plausible knowledge from a set of hy- potheses generated from the whole corpus. In particular, we introduce two statistical measures of hypotheses, Local Plausibility and Global Plausibility, and describe how these measures are determined iteratively. The proposed method will be incorporated into the tool kit for linguistic knowledge acquisition which we are now developing. 1 Introduction Current technologies in natural language process- ing are not so mature as to make general purpose systems applicable to any domains; therefore rapid customization of linguistic knowledge to the sub- language of an application domain is vital for the development of practical systems. In the currently working systems, such customization has been car- ried out manually by linguists or lexicographers with time-consuming effort. We have already proposed a mechanism which acquires sublanguage-specific linguistic knowledge from parsing failures and which can be used as a tool for linguistic knowledge customization (Kiyono and Tsujii, 1993; Kiyono and Tsujii, 1994). Our ap- proach is characterized by a mixture of symbolic and statistical approaches to grammatical knowledge ac- quisition. Unlike probabilistic parsing, proposed by (Fujisaki et al., 1989; Briscoe and Carroll, 1993), *also a staff member of Matsushita Electric Industrial Co.,Ltd., Shinagawa, Tokyo, JAPAN. 72 which assumes the prior existence of comprehensive linguistic knowledge, our system can suggest new pieces of knowledge including CFG rules, subcate- gorization frames, and other lexical features. It also differs from previous proposals on lexical acquisi- tion using statistical measures such as (Church et al., 1991; Brent, 1991; Brown et al., 1993) which ei- ther deny the prior existence of linguistic knowledge or use linguistic knowledge in ad hoc ways. Our system consists of two components: (1) the rule-based component, which detects incompleteness of the existing knowledge and generates a set of hy- potheses of new knowledge and (2) the corpus-based component which selects plausible hypotheses on the basis of their statistical behaviour. As the rule-based component has been explained in our previous pa- pers, in this paper we focus on the corpus-based component. After giving a brief explanation of the framework, we describe a data structure called Hypothesis Graph which plays a crucial role in the corpus-based pro- cess, and then introduce two statistical measures of hypotheses, Global Plausibility and Local Plausibil- ity, which are iteratively determined to select a set of plausible hypotheses. An experiment which shows the effectiveness of our method is …

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