Prediction of thematic rank for structured semantic role labeling

Authors:
Weiwei Sun;Zhifang Sui;Meng Wang
Affiliations:
Peking University, Key Laboratory of Computational Linguistics, Ministry of Education, China;Peking University, Key Laboratory of Computational Linguistics, Ministry of Education, China;Peking University, Key Laboratory of Computational Linguistics, Ministry of Education, China
Venue:
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Year:
2009

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Abstract

In Semantic Role Labeling (SRL), it is reasonable to globally assign semantic roles due to strong dependencies among arguments. Some relations between arguments significantly characterize the structural information of argument structure. In this paper, we concentrate on thematic hierarchy that is a rank relation restricting syntactic realization of arguments. A loglinear model is proposed to accurately identify thematic rank between two arguments. To import structural information, we employ re-ranking technique to incorporate thematic rank relations into local semantic role classification results. Experimental results show that automatic prediction of thematic hierarchy can help semantic role classification.