Abstract
Semantic role expresses the underlying relations that an argument has with its governing predicate. Prepositional phrase semantic role labeling concentrates on such relations indicated by prepositional phrases. Previously, the problem has been formulated as a word sense disambiguation (WSD) problem and contextual words are used as important features. In the past years, there has been a growing interests in general semantic role labeling (SRL). Therefore, it would be interesting to compare the previous contextual features with argument related features specifically designed for semantic role labeling. In experiments, we showed that the argument related features are much better than the contextual features, improving classification accuracy from 84.96% to 90.25% on a 6 role task and 71.47% to 75.93% on a 33 role task. To further investigate dependency between frame elements, we also introduced new features based on semantic frame that consider the governing predicate, preposition, and content phrase at the same time. The use of frame based features further improves the accuracy to 91.25% and 83.48% on both tasks respectively. In the end, we found that by treating prepositional phrases carefully, the overall performance of a semantic role labeling system can be improved significantly.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
O’Hara, T., Wiebe, J.: Preposition semantic classification via Treebank and FrameNet. In: Proceedings of Computational Natural Language Learning (CoNLL 2003), Edmonton (2003)
Ye, P., Baldwin, T.: Semantic role labelling of prepositional phrases. ACM Transactions on Asian Language Information Processing 5(3), 228–244 (2006)
Dahlmeier, D., Ng, H.T., Schultz, T.: Joint learning of preposition senses and semantic roles of prepositional phrases. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 450–458 (2009)
Vapnik, V.: Statistical Learning Theory. John Wiley and Sons Inc., Chichester (1998)
Surdeanu, M., Turmo, J.: Semantic role labeling using complete syntactic analysis. In: Proceedings of the CoNLL share task: semantic role labeling (2005)
Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wen, D., Dou, Q. (2010). Exploiting Frame Information for Prepositional Phrase Semantic Role Labeling. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-13059-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13058-8
Online ISBN: 978-3-642-13059-5
eBook Packages: Computer ScienceComputer Science (R0)