计算机科学
命名实体识别
自然语言处理
人工智能
语义学(计算机科学)
程序设计语言
经济
管理
任务(项目管理)
作者
Xunwei Yin,Shuang Zheng,Quanmin Wang
标识
DOI:10.1109/icivc52351.2021.9526957
摘要
Named entity recognition (NER) is a basic technology of Natural Language Processing (NLP). It is mainly used to identify entities and entity types. Compared with traditional entity recognition, fine-grained entity recognition can provide more precise semantics. In order to improve the effect of fine-grained Chinese N ER, w e propose a model based on RoBERTa-WWM-BiLSTM-CRF and compare it with other high-quality models. The experimental results show that this model has better effect on the CLUENER2020 dataset of fine-grained Chinese NER.
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