Fusion of Multiple Features for Chinese Named Entity Recognition Based on CRF Model

摘要：

This paper presents the ability of Conditional Random Field (CRF) combining with multiple features to perform robust and accurate Chinese Named Entity Recognition.We describe the multiple feature templates including local feature templates and global feature templates used to extract multiple features with the help of human knowledge.Besides,we show that human knowledge can reasonably smooth the model and thus the need of training data for CRF might be reduced.From the experimental results on Peoples Daily corpus,we can conclude that our model is an effective pattern to combine statistical model and human knowledge.And the experiments on another data set also confirm the above conclusion,which shows that our features have consistence on different testing data.

关键词： Named Entity Recognition Conditional Random Field multiple features

作者: Yuejie Zhang Zhiting Xu Tao Zhang

作者单位: Department of Computer Science & Engineering,Shanghai Key Laboratory of Intelligent Information Proc School of Information Management & Engineering,Shanghai University of Finance & Economics,Shanghai 2

会议类型: 国际会议

会议名称: 4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 95-106

在线出版日期: 2008-01-16（万方平台首次上网日期，不代表论文的发表时间）