Recognizing Location Names from Chinese Texts Based on Max-Margin Markov Network

摘要：

This paper presents a novel method of recognizing location names from Chinese texts based on Max-Margin Markov Network (M3Net) owing to its ability to exploit very high dimensional feature spaces (using the kernel trick) while at the same time dealing with structured data compared with Support Vector Machine (SVM) and Conditional Random Fields (CRFs). In our model, the character itself, character-based part-of-speech (POS) tag, the information whether a character appears in the location name characteristic word table and context information are extracted as the features. The F-measure is up to 90.57％ based on 1-order M3Net which is better than that based on either SVM or CRFs in open test on MSRA dataset.

关键词： SVM CRFs M3Net named entity recognition

作者: Lishuang LI Zhuoye DING Degen HUANG

作者单位: Dalian University of Technology Dalian,Liaoning,China

会议类型: 国际会议

会议名称: The 2008 IEEE International Conference on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-19（万方平台首次上网日期，不代表论文的发表时间）