Chinese semantic role labeling using CRFs and SVMs

Author

Tan, Yongmei ; Wang, Xu ; Chen, Yong

Author_Institution

Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2009

fDate

24-27 Sept. 2009

Firstpage

1

Lastpage

5

Abstract

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%.

Keywords

computational linguistics; information retrieval; support vector machines; CRF; Chinese semantic role labeling; SVM; computational linguistics; document retrieval; information extraction; machine translation; question answering; Computational linguistics; Data mining; Gold; Information retrieval; Labeling; Natural languages; Performance evaluation; Support vector machines; Teeth; Text recognition; Conditional Random Fields; Semantic Role Labeling; Support Vector Machines; Tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Language Processing and Knowledge Engineering, 2009. NLP-KE 2009. International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4244-4538-7

Electronic_ISBN

978-1-4244-4540-0

Type

conf

DOI

10.1109/NLPKE.2009.5313827

Filename

5313827