Chunking Japanese Compound Functional Expressions by Machine Learning

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  • 機械学習を用いた日本語機能表現のチャンキング

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The Japanese language has many compound functional expressions which consist of more than one word including both content words and functional words. They are very important for recognizing the syntactic structures of Japanese sentences and for understanding their semantic contents. We formalize detection of Japanese compound functional expressions as a chunking problem against a morpheme sequence, and propose to learn a detector of them using a machine learning method. The chunker YamCha based on Support Vector Machines (SVMs) is applied to this task. Through experimental evaluation, we achieve the cross validation result of the F-measure as 92, when the number of morphemes constituting a compound functional expression, and the position of each morpheme within a functional expression are considered as features of SVM.

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