FINB: a Japanese named entity recognition model based on multi-feature integration method

Abstract Named entity recognition (NER) is a critical task in natural language processing. It extracts entity information such as person, location, and organization by predicting various categories of label types and entity spans in text. Nowadays, NER has achieved good recognition results in English text by machine learning. However, satisfactory recognition results cannot be achieved when processing text in Japanese, due to the diversity of the text composition and the particularity of the language itself. Compared with English text, which different words are marked by spaces, there is no clear separation mark between two words in Japanese. Simultaneously, Japanese text includes three types of representation methods, which is different from English text which only consists of English alphabet. In order to solve the above problems, a feature integration network with BERT called FINB is introduced in this paper based on multi-feature integration, which can integrate pronunciation features and glyph features of Japanese into the model to obtain more semantic information. The experiments for verification are conducted on the Kyoto University Web Document Leads Corpus called KWDLC and the Japanese Wikipedia dataset, which both prove that the proposed method can improve the recognition of named entities in Japanese effectively.