A pattern-learning based, hybrid model for the syntactic analysis of structural relationshi.pdf

A Pattern-Learning Based, Hybrid Model for the Syntactic Analysis of Structural Relationships among Japanese Clauses Akitoshi OKUMURA Kazunori MURAKI Kiyoshi YAMABANA NEC Corp. C C Information Technology Research Laboratories 4-1-1 Miyazaki, Miyamae-ku, Kawasaki 216, JAPAN okumura%mtl.cl.nec.co.jp@ Abstract This paper presents a model for analyzing Japanese compound sentences by taking advantage of users examples. Syntactic analysis of the Japanese compound sentences is one of the most difficult problems for machine translation (MT) systems. One particularly difficult problem is selecting, from all possible candidates, the correct global structure of an individual sentence, i.e. recognizing the set of structural relationships actually linking the various clauses in a compound sentence. MT systems are generally equipped with correction tools for users to modify the incorrect results of the system analyses. Users must use such tools over and over again to modify the same kind of sentences, however, because there is no effective method to memorize the previously corrected examples. The authors here propose a pattern-learning based, hybrid model for analyzing structural relationships among Japanese clauses. The model consists of rule-based modules and a learning module which memorizes correct global structures as well as incorrect ones. All structures are memorized in the form of five-region patterns which are represented by salient features. The model is investigated in comparison with the properties of connectionist approaches, and then the validity of the model is supported by the the results of preliminary experiments. 1 Introduction This paper presents a model for analyzing Japanese compound sentences by taking advantage of users examples. Syntactic analysis of Japanese compound sentences is one of the most difficult problems for machine translation (MT) systems. It requires two kinds of analyses: one for local structures, that is, to