Knowledge Commons of Institute of Automation,CAS
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network | |
Dianbo Sui1,2![]() ![]() ![]() ![]() | |
2019-11 | |
会议名称 | Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) |
会议日期 | 2019-11 |
会议地点 | HongKong |
摘要 | The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model. |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48925 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation 2.University of Chinese Academy of Sciences 3.Beijing Unisound Information Technology Co., Ltd |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Dianbo Sui,Yubo Chen,Kang Liu,et al. Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
D19-1396.pdf(909KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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