A Survey of Domain-Specific Named-Entity Recognition in Japanese

Abstract

Named-entity recognition (NER) technologies have been developed for the general knowledge domain, and the extension of NER to specific domains has now become a topic of active research. In this paper, we provide a survey of recent studies on domain-specific NER technologies for the Japanese language. We focus on aspects of different applications such as their targets and purposes as well as the knowledge domains considered. Our results show that chemistry is the most fundamental knowledge domain represented in these works, along with medical, financial, machining, literature, and food domains. Most of the systems included in the survey utilized machine learning methods such as BiLSTM-CRF and BERT, though some studies used rules and dictionaries in addition to these approaches.