Japanese Named Entity Recognition Using Structural Natural Language Processing

This paper presents an approach that uses structural information for Japanese named entity recognition (NER). Our NER system is based on Support Vector Machine (SVM), and utilizes four types of structural information: cache features, coreference relations, syntactic features and caseframe features, which are obtained from structural analyses. We evaluated our approach on CRL NE data and obtained a higher F-measure than existing approaches that do not use structural information. We also conducted experiments on IREX NE data and an NE-annotated web corpus and confirmed that structural information improves the performance of NER.