Joint multi-view character embedding model for named entity recognition of Chinese car reviews

Named entity recognition (NER) has always been an important research task in information extraction and knowledge graph construction. Due to the randomness of Chinese user-generated reviews, character substitution and informal expression are very common. Its widespread phenomenon leads to that Chinese car reviews NER is still a major challenge. In this paper, we propose a joint multi-view character embedding model for Chinese NER (JMCE-CNER) of car reviews. Firstly, deeper character features are extracted from pronunciation, radical, and glyph views to generate the multi-view character embedding. Secondly, a car domain dictionary is constructed for providing accurate word-level information. Thirdly, the multi-view character embedding and the word-level embedding are jointly fed into the deep learning model to perform the Chinese car reviews NER. The experimental datasets of Chinese car reviews are obtained by manual annotation, containing four types of entities, namely brand, model, attribute and structure of the car. The experimental results on the Chinese car review datasets demonstrate that our proposed model achieves the optimal performance compared with the other state-of-the-art models. Furthermore, the model substantially reduces the impact of character substitution and informal expression on performing NER tasks.