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Exploring Various Features in Semantic Role Labeling

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4 Author(s)
Hongling Wang ; Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou ; Guodong Zhou ; Qiaoming Zhu ; Peide Qian

This paper explores the contributions of various features in semantic role labeling. Moreover, an optimal set of features is selected using a greedy strategy. Finally, an effective headword-driven pruning algorithm is proposed to filter out irrelavant instances. Evaluation on the CoNLL'2005 SRL benchmark corpus shows that our method achieved comparable performance with the best-reported ones on a single automatic parse tree.

Published in:

Advanced Language Processing and Web Information Technology, 2008. ALPIT '08. International Conference on

Date of Conference:

23-25 July 2008