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

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. • This paper proposes an RGE-NER model to solve the NER problem for Chinese car reviews. The model innovatively combines referent graph and deep learning models. • Word-based and pronunciation-based methods are designed to expand the index range of entities in lexicon, which reduces the impact of irregular text expressions. • The latest BERT-based character vectors and the character-level candidate entities are jointly embedded into the deep learning model to perform the NER. • The embedding weights of candidate entities are measured by exploiting the global interdependence between candidate entities instead of algorithm learning, which significantly reduces the time complexity while improving the model performance. [ABSTRACT FROM AUTHOR]