Knowledge Commons of Institute of Automation,CAS
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism | |
Pengfei Cao; Yubo Chen; Kang Liu; Jun Zhao; Shengping Liu | |
2018-11-01 | |
会议名称 | The 2018 Conference on Empirical Methods in Natural Language Processing |
会议日期 | October 31 - November 4, 2018 |
会议地点 | Brussels, Belgium |
出版者 | Association for Computational Linguistics |
摘要 | Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the taskspecific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit selfattention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-ofthe-art methods. |
收录类别 | EI |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52145 |
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.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Pengfei Cao,Yubo Chen,Kang Liu,et al. Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism[C]:Association for Computational Linguistics,2018. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
D18-1017 (1).pdf(422KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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