Japanese Dependency Parsing Using Co-Occurrence Information and a Combination of Case Elements

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 833–840, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Japanese Dependency Parsing Using Co-occurrence Information and a Combination of Case Elements Takeshi Abekawa Graduate School of Education University of Tokyo abekawa@p.u-tokyo.ac.jp Manabu Okumura Precision and Intelligence Laboratory Tokyo Institute of Technology oku@pi.titech.ac.jp Abstract In this paper, we present a method that improves Japanese dependency parsing by using large-scale statistical information. It takes into account two kinds of informa- tion not considered in previous statistical (machine learning based) parsing meth- ods: information about dependency rela- tions among the case elements of a verb, and information about co-occurrence re- lations between a verb and its case ele- ment. This information can be collected from the results of automatic dependency parsing of large-scale corpora. The results of an experiment in which our method was used to rerank the results obtained using an existing machine learning based parsing method showed that our method can im- prove the accuracy of the results obtained using the existing method. 1 Introduction Dependency parsing is a basic technology for pro- cessing Japanese and has been the subject of much research. The Japanese dependency structure is usually represented by the relationship between phrasal units called bunsetsu, each of which con- sists of one or more content words that may be followed by any number of function words. The dependency between two bunsetsus is direct from a dependent to its head. Manually written rules have usually been used to determine which bunsetsu another bunsetsu tends to modify, but this method poses problems in terms of the coverage and consistency of the rules. The recent availability of larger-scale corpora an- notated with dependency information has thus re- sulted in more work on statistical dependency analysis technologies that use machine learning al- gorithms (Kudo and Matsumoto, 2002; Sassano, 2004; Uchimoto et al., 1999; Uchimoto et al., 2000). Work on statistical Japanese dependency analy- sis has usually assumed that all the dependency re- lations in a sentence are independent of each other, and has considered the bunsetsus in a sentence in- dependently when judging whether or not a pair of bunsetsus is in a dependency relation. In judg- ing which bunsetsu a bunsetsu modifies, this type of work has used as features the information of two bunsetsus, such as the head words of the two bunsetsus, and the morphemes at the ends of the bunsetsus (Uchimoto et al., 1999). It is necessary, however, to also consider features for the contex- tual information of the two bunsetsus. One such feature is the constraint that two case elements with the same case do not modify a verb. Statistical Japanese dependency analysis takes into account syntactic information but tends not to take into account lexical information, such as co- occurrence between a case element and a verb. The recent availability of more corpora has en- abled much information about dependency rela- tions to be obtained by using a Japanese …

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