Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media

摘要：

　　Named Entity Recognition(NER)is a tough task in Chi-nese social media due to a large portion of informal writings.Existing research uses only limited in-domain annotated data and achieves low performance.In this paper,we utilize both limited in-domain data and enough out-of-domain data using a domain adaptation method.We pro-pose a multichannel LSTM-CRF model that employs different channels to capture general patterns,in-domain patterns and out-of-domain pat-terns in Chinese social media.The extensive experiments show that our model yields 9.8%improvement over previous state-of-the-art methods.We further ifnd that a shared embedding layer is important and ran-domly initialized embeddings are better than the pretrained ones.

关键词： multichannel Named Entity Recognition Chinese socialmedia

作者: Chuanhai Dong Huijia Wu Jiajun Zhang Chengqing Zong

作者单位: National Laboratory of Pattern Recognition,CASIA,Beijing,China;University of Chinese Academy of Scie National Laboratory of Pattern Recognition,CASIA,Beijing,China;University of Chinese Academy of Scie

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）