Semantic type disambiguation for Japanese verbs

Abstract

The interest has been increasing in recent years in extracting and analyzing evaluations and opinions of service or products from large bodies of text. It is important to classify predicates according to sense because whether or not a statement includes the speaker's opinion depends strongly on its predicate. It is generally assumed that Japanese part-of-speech (POS) for predicates is classified according to sense; however, the POS classifications differ from their semantic classification. On this subject, semantic types, which aim to classify predicates, have been proposed. In this paper, we describe semantic types and present our construction of a disambiguator for Japanese verbs. Specifically, we constructed this disambiguator using a support vector machine by building feature vectors. We used semantic categories of noun and results of morphological analysis for the feature vectors. We then achieved 69.9% accuracy of disambiguation for newspaper articles using 10-fold cross-validation.