Referent graph embedding model for name entity recognition of Chinese car reviews
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Name entity recognition,Referent graph,Deep learning,Natural language process
论文评审过程：Received 29 May 2021, Revised 28 September 2021, Accepted 30 September 2021, Available online 2 October 2021, Version of Record 9 October 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107558
