Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism | |
Pengfei Cao1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2 | |
2020-10-30 | |
会议日期 | October 30 - Novermber 1, 2020 |
会议地点 | Hainan, China |
英文摘要 | Named entity recognition (NER) aims to identify text spans that mention named entities and classify them into pre-defined categories. For Chinese NER task, most of the existing methods are character-based sequence labeling models and achieve great success. However, these methods usually ignore lexical knowledge, which leads to false prediction of entity boundaries. Moreover, these methods have difficulties in capturing tag dependencies. In this paper, we propose an Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism (AMMNHT) to address all above problems. Specifically, to reduce the errors of predicting entity boundaries, we propose an adaptive multi-pass memory network to exploit lexical knowledge. In addition, we propose a hierarchical tagging layer to learn tag dependencies. Experimental results on three widely used Chinese NER datasets demonstrate that our proposed model outperforms other stateof-the-art methods. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52185] |
专题 | 模式识别国家重点实验室_自然语言处理 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Pengfei Cao,Yubo Chen,Kang Liu,et al. Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism[C]. 见:. Hainan, China. October 30 - Novermber 1, 2020. |
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