Chinese Semantic Role Labeling Using CRFs and SVMs

摘要：

There is a widely held belief in the NLP and computational linguistics communities that identifying and defining roles of predicate arguments in a sentence has a lot of potential for and is a significant step toward improving important applications such as document retrieval, machine translation, question answering and information extraction. In this paper, we present an semantic role labeling (SRL) system for Chinese that exploits many aspects of the rich features of the languages. Finally, we compare system based on CRFs and SVMs. The experiment yields a global SRL FB1 score of 92.89%.

关键词： Tracking Semantic Role Labeling Conditional Random Fields Support Vector Machines

作者: Yongmei Tan Xu Wang Yong Chen

作者单位: Beijing University of Posts and Telecommunications. Beijing 100876, China

会议类型: 国际会议

会议名称: International Conference on Natural Language Processing and Knowledge Engineering(IEEE自然语言处理与知识工程国际会议 IEEE NLP-KE 2009)

会议地点: 大连

会议语种:英文

页码: 1-5

在线出版日期: 2009-09-24（万方平台首次上网日期，不代表论文的发表时间）