Exploiting Frame Information for Prepositional Phrase Semantic Role Labeling

  • Dunwei Wen
  • Qing Dou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6085)

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.

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References

  1. 1.
    O’Hara, T., Wiebe, J.: Preposition semantic classification via Treebank and FrameNet. In: Proceedings of Computational Natural Language Learning (CoNLL 2003), Edmonton (2003)Google Scholar
  2. 2.
    Ye, P., Baldwin, T.: Semantic role labelling of prepositional phrases. ACM Transactions on Asian Language Information Processing 5(3), 228–244 (2006)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Vapnik, V.: Statistical Learning Theory. John Wiley and Sons Inc., Chichester (1998)MATHGoogle Scholar
  5. 5.
    Surdeanu, M., Turmo, J.: Semantic role labeling using complete syntactic analysis. In: Proceedings of the CoNLL share task: semantic role labeling (2005)Google Scholar
  6. 6.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dunwei Wen
    • 1
  • Qing Dou
    • 1
  1. 1.School of Computing & Information SystemsAthabasca UniversityAthabascaCanada

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