tailieunhanh - Báo cáo khoa học: "Semantic Analysis of Japanese Noun Phrases: A New Approach to Dictionary-Based Understanding"

This paper presents a new m e t h o d of analyzing Japanese noun phrases of the form N1 no 5/2. The Japanese postposition no roughly corresponds to of, but it has much broader usage. The method exploits a definition of N2 in a dictionary. For example, rugby no coach can be interpreted as a person who teaches technique in rugby. We illustrate the effectiveness of the m e t h o d by the analysis of 300 test noun phrases. | Semantic Analysis of Japanese Noun Phrases A New Approach to Dictionary-Based Understanding Sadao Kurohashi and Yasuyuki Sakai Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo Kyoto 606-8501 Japan Abstract This paper presents a new method of analyzing Japanese noun phrases of the form N1 no Nz- The Japanese postposition no roughly corresponds to of but it has much broader usage. The method exploits a definition of Nz in a dictionary. For example rugby no coach can be interpreted as a person who teaches technique in rugby. We illustrate the effectiveness of the method by the analysis of 300 test noun phrases. 1 Introduction The semantic analysis of Japanese noun phrases of the form Al no Nz is one of the difficult problems which cannot be solved by the current efforts of many researchers. Roughly speaking Japanese noun phrase Al no Nz corresponds to English noun phrase Nz of Ny. However the Japanese postposition no has much broader usage than of as follows watashi T no kuruma car possession tsukue desk no ashi leg whole-part gray no seihuku uniform modification senmonka expert no chousa study agent rugby no coach subject yakyu baseball no senshu player category kaze cold no virus result ryokou travel no jyunbi preparation purpose toranpu card no tejina trick instrument The conventional approach to this problem was to classify semantic relations such as possession whole-part modification and others. Then classification rules were crafted by hand or detected from relation-tagged examples by a machine learning technique Shimazu et al. 1987 Sumita et al. 1990 Tomiura et al. 1995 Kurohashi et al. 1998 . The problem in such an approach is to set up the semantic relations. For example the above examples and their classification came from the IPA nominal dictionary InformationTechnology Promotion Agency Japan 1996 . Is it possible to find clear boundaries among subject category result purpose instrument and others No matter how .