Morphological Analysis with Pointwise Predictors

Abstract

This paper proposes a pointwise approach to Japanese morphological analysis that decomposes the process into word segmentation and part-of-speech (POS) tagging. The pointwise approach refers, as features, only to the surface information of the input and not relies on any prediction results such as word boundaries or POS tags. This design allows us to use a variety of linguistic resources flexibly. This characteristic enables a fast and low-cost domain adaptation with a minimum amount of annotation. An evaluation was performed on a well-resourced general domain morphological task, and it was found that the proposed method achieved results comparable to those of existing methods such as CRFs and morpheme n-gram models. In addition, a domain adaptation experiment showed that the proposed method is able to achieve an effective domain adaptation with a smaller amount of annotations.