Learning Probabilistic Subcategorization Preference by Identifying Case Dependencies and Optimal Noun Class Generalization Level

Learning Probabilistic Subcategorization Preference by Identifying Case Dependencies and Optimal Noun Class Generalization Level* Takehito Utsuro Yuji Matsumoto Graduate School of Information Science, Nara Institute of Science and Technology 8916-5, Takayama-cho, Ikoma-shi, Nara, 630-01, JAPAN {ut suro ,matsu}l~is. aist-nara, ac. jp Abstract This paper proposes a novel method of learning probabilistic subcategorization preference. In the method, for the purpose of coping with the ambi- guities of case dependencies and noun class gen- eralization of argument/adjunct nouns, we intro- duce a data structure which represents a tuple of independent partial subcategorization frames. Each collocation of a verb and argument/adjunct nouns is assumed to be generated from one of the possible tuples of independent partial subcatego- rization frames. Parameters of subcategorization preference are then estimated so as to maximize the subcategorization preference function for each collocation of a verb and argument/adjunct nouns in the training corpus. We also describe the results of the experiments on learning probabilistic sub- categorization preference from the EDR Japanese bracketed corpus, as well as those on evaluating the performance of subcategorization preference. 1 Introduction In corpus-based NLP, extraction of linguistic knowl- edge such as lexical/semantic collocation is one of the most important issues and has been intensively stud- ied in recent years. In those research, extracted lex- ical/semantic collocation is especially useful in terms of ranking parses in syntactic analysis as well as au- tomatic construction of lexicon for NLP. For example, in the context of syntactic disam- biguation, Black (1993) and Magerman (1995) pro- posed statistical parsing models based-on decision- tree learning techniques, which incorporated not only syntactic but also lexical/semantic information in the decision-trees. As lexical/semantic informa- tion, Black (1993) used about 50 semantic categories, while Magerman (1995) used lexicai forms of words. Collins (1996) proposed a statistical parser which is based on probabilities of dependencies between head-words in the parse tree. In those works, lexi- cal/semantic collocation are used for ranking parses in syntactic analysis. *The authors would like to thank Dr. Hang Li of NEC C&:C Research Laboratories, Dr. Kentaro Inui of Tokyo Institute of Technology, Dr. Koiti Hasida of Electrotech- nical Laboratory, Dr. Tak_ashi Miyata of Nara Institute of Science and Technology, and also anonymous reviewers of ANLP97 for valuable comments on this work. On the other hand, in the context of automatic lex- icon construction, the emphasis is mainly on the ex- traction of lexical/semantic collocational knowledge of specific words rather than its use in sentence parsing. For example, Haruno (1995) applied an information- theoretic data compression technique to corpus-based case frame learning, and proposed a method of find- ing case frames of verbs as compressed representation of verb-noun collocational data in corpus. The work concentrated on the extraction of declarative repre- sentation of case frames and did not consider their performance in sentence parsing. This paper focuses on extracting lexical/semantic collocational knowledge of verbs for the purpose of ap- plying it to ranking parses in syntactic analysis. More specifically, we propose a novel method for learning parameters for calculating subcategorization …

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