Employing Auto-annotated Data for Person Name Recognition in Judgment Documents

摘要：

　　In the last decades,named entity recognition has been extensivelystudied with various supervised learning approaches depend on massive labeled data.In this paper,we focus on person name recognition in judgment documents.Owingto thelackofhuman-annotated data,weproposeajoint learningapproach,namely Aux-LSTM,to use a large scale of auto-annotated data to help human-annotated data(in a small size)for person name recognition.Specifically,our approach first develops an auxiliary Long Short-Term Memory(LSTM)repre-sentation by training the auto-annotated data and then leverages the auxiliary LSTM representation toboosttheperformanceofclassifiertrained on thehuman-annotated data.Empirical studies demonstrate the effectiveness of our proposed approach to person name recognition in judgment documents with both human-annotated and auto-annotated data.

关键词： named entity recognition auto-annotated data LSTM

作者: Limin Wang Qian Yan Shoushan Li Guodong Zhou

作者单位: Natural Language Processing Lab,School of Computer Science and Technology,Soochow University,China

会议类型: 国内会议

会议名称: 第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

会议地点: 南京

会议语种:英文

页码: 1-12

在线出版日期: 2017-10-13（万方平台首次上网日期，不代表论文的发表时间）