Bunsetsu identification using category-exclusive rules

Authors:
Masaki Murata;Kiyotaka Uchimoto;Qing Ma;Hitoshi Isahara
Affiliations:
Communications Research Laboratory, Ministry of Posts and Telecommunications, Kobe, Japan;Communications Research Laboratory, Ministry of Posts and Telecommunications, Kobe, Japan;Communications Research Laboratory, Ministry of Posts and Telecommunications, Kobe, Japan;Communications Research Laboratory, Ministry of Posts and Telecommunications, Kobe, Japan
Venue:
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Year:
2000

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Abstract

This paper describes two new bunsetsu identification methods using supervised learning. Since Japanese syntactic analysis is usually done after bunsetsu identification, bunsetsu identification is important for analyzing Japanese sentences. In experiments comparing the four previously available machine-learning methods (decision tree, maximum-entropy method, example-based approach and decision list) and two new methods using category-exclusive rules, the new method using the category-exclusive rules with the highest similarity performed best.