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Automatic Extraction of Hierarchical Relations from Text

  • Ting Wang
  • Yaoyong Li
  • Kalina Bontcheva
  • Hamish Cunningham
  • Ji Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4011)

Abstract

Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.

Keywords

Support Vector Machine Support Vector Machine Model Semantic Feature Automatic Extraction Dependency Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
  2. 2.
    Annotation Guidelines for Entity Detection and Tracking (EDT) Version 4.2.6, (2004), http://www.ldc.upenn.edu/Projects/ACE/docs/EnglishEDTV4-2-6.PDF
  3. 3.
    Annotation Guidelines for Relation Detection and Characterization (RDC) Version 4.3.2, (2004), http://www.ldc.upenn.edu/Projects/ACE/docs/EnglishRDCV4-3-2.PDF
  4. 4.
    Appelt, D.: An Introduction to Information Extraction. Artificial Intelligence Communications 12(3), 161–172 (1999)Google Scholar
  5. 5.
    Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: Proceedings of 42th Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, July 21-26 (2004)Google Scholar
  6. 6.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, Philadelphia (July 2002)Google Scholar
  7. 7.
    Freitag, D., McCallum, A.: Information extraction with HMM structures learned by stochastic optimization. In: Proceedings of the 7th Conference on Artificial Intelligence (AAAI 2000) and of the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI 2000), pp. 584–589. AAAI Press, Menlo Park (2000)Google Scholar
  8. 8.
    Gaizauskas, R., Hepple, M., Saggion, H., Greenwood, M.A., Humphreys, K.: SUPPLE: A Practical Parser for Natural Language Engineering Applications. Technical report CS–05–08, Department of Computer Science, University of Sheffield (2005)Google Scholar
  9. 9.
    Handschuh, S., Staab, S., Ciravegna, F.: S-CREAM — Semi-automatic CREAtion of Metadata. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS, vol. 2473, p. 358. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)CrossRefGoogle Scholar
  11. 11.
    Kambhatla, N.: Combining lexical, syntactic and semantic features with Maximum Entropy models for extracting relations. In: Proceedings of 42th Annual Meeting of the Association for Computational Linguistic, Barcelona, Spain, July 21-26 (2004)Google Scholar
  12. 12.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, pp. 282–289. Morgan Kaufmann, San Francisco (2001)Google Scholar
  13. 13.
    Li, Y., Bontcheva, K., Cunningham, H.: SVM Based Learning System For Information Extraction. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Deterministic and Statistical Methods in Machine Learning. LNCS, vol. 3635, pp. 319–339. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Lin, D.: Dependency-based Evaluation of MINIPAR. In: Workshop on the Evaluation of Parsing Systems, Granada, Spain (May 1998)Google Scholar
  15. 15.
    Miller, A.: WordNet: An On-line Lexical Resource. Special issue of the Journal of Lexicography 3(4) (1990)Google Scholar
  16. 16.
    Motta, E., VargasVera, M., Domingue, J., Lanzoni, M., Stutt, A., Ciravegna, F.: MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS, vol. 2473, p. 379. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Wang, T., Bontcheva, K., Li, Y., Cunningham, H.: D2.1.2. Ontology-Based Information Extraction. SEKT Deliverable D2.1.2 (2005), http://www.sekt-project.org/rd/deliverables/index_html/
  18. 18.
    Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. Journal of Machine Learning Research, 1083–1106 (2003)Google Scholar
  19. 19.
    Zhou, G., Su, J., Zhang, J., Zhang, M.: Combining Various Knowledge in Relation Extraction. In: Proceedings of the 43th Annual Meeting of the Association for Computational Linguistics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ting Wang
    • 1
    • 2
  • Yaoyong Li
    • 1
  • Kalina Bontcheva
    • 1
  • Hamish Cunningham
    • 1
  • Ji Wang
    • 2
  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK
  2. 2.Department of ComputerNational University of Defense TechnologyChangsha, HunanP.R. China

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