Referent graph embedding model for name entity recognition of Chinese car reviews

Abstract

Name entity recognition (NER) is one of the most basic tasks for extracting information from Internet text. Chinese NER remains a major challenge due to the language complexity. Although researchers have recently used domain knowledge to embed word-level information into the deep learning models to deal with the Chinese NER, they have not considered the global interdependence between word-level information, i.e., the entities in the same document should be semantically related to each other. In addition, domain knowledge often cannot be used efficiently due to the presence of irregular expressions in the Internet text, such as abbreviations and aliases. In this paper, we propose a referent graph embedding model for the NER, specifically concentrating on the Chinese car review. First, domain knowledge is used to generate character-level candidate entities and model the global interdependence between these entities based on the referent graph model. Second, the latest BERT-based character vectors and the character-level candidate entities are jointly embedded into the deep learning model to perform the NER. Last, Chinese car reviews are collected and labeled for use as the experimental dataset. The experimental results demonstrate the efficiency and effectiveness of the proposed model for the Chinese car NER task compared with the other start-of-the-art models.