Advertisement

Automatic Relation Extraction with Model Order Selection and Discriminative Label Identification

  • Chen Jinxiu
  • Ji Donghong
  • Tan Chew Lim
  • Niu Zhengyu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3651)

Abstract

In this paper, we study the problem of unsupervised relation extraction based on model order identification and discriminative feature analysis. The model order identification is achieved by stability-based clustering and used to infer the number of the relation types between entity pairs automatically. The discriminative feature analysis is used to find discriminative feature words to name the relation types. Experiments on ACE corpus show that the method is promising.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Defense Advanced Research Projects Agency.: Proceedings of the Sixth Message Understanding Conference (MUC-6). Morgan Kaufmann Publishers, Inc., San Francisco (1995)Google Scholar
  2. 2.
    Califf, M.E., Mooney, R.J.: Relational Learning of Pattern-Match Rules for Information Extraction. AAAI, Menlo Park (1999)Google Scholar
  3. 3.
    Brin, S.: Extracting patterns and relations from world wide web. In: Proc. of WebDB Workshop at 6th International Conference on Extending Database Technology, pp. 172–183 (1998)Google Scholar
  4. 4.
    Sudo, K., Sekine, S., Grishman, R.: An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition. In: Proceedings of ACL, Sapporo, Japan (2003)Google Scholar
  5. 5.
    Yangarber, R., Grishman, R., Tapanainen, P., Huttunen, S.: Unsupervised discovery of scenario-level patterns for information extraction. In: Proceedings of the Applied Natural Language Processing Conference, Seattle, WA (2000)Google Scholar
  6. 6.
    Agichtein, E., Gravano, L.: Snowball: Extracting Relations from large Plain-Text Collections. In: Proc. of the 5th ACM International Conference on Digital Libraries (2000)Google Scholar
  7. 7.
    Hasegawa, T., Sekine, S., Grishman, R.: Discovering Relations among Named Entities from Large Corpora. In: Proceeding of Conference ACL, Barcelona, Spain (2004)Google Scholar
  8. 8.
    Zelenko, D., Aone, C., Richardella, A.: Kernel Methods for Relation Extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia (2002)Google Scholar
  9. 9.
    Soderland, S.: Learning information extraction rules for semi-structured and free text. Machine Learning 31(1-3), 233–272 (1999)CrossRefGoogle Scholar
  10. 10.
    Lange, T., Braun, M., Roth, V., Buhmann, J.M.: Stability-Based Model Selection. Advances in Neural Information Processing Systems 15 (2002)Google Scholar
  11. 11.
    Levine, E., Domany, E.: Resampling Method for Unsupervised Estimation of Cluster Calidity. Neural Computation 13, 2573–2593 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Niu, Z., Ji, D., Tan, C.L.: Document Clustering Based on Cluster Validation. In: CIKM 2004, Washington, DC, USA, November 8-13 (2004)Google Scholar
  13. 13.
    Roth, V., Lange, T.: Feature Selection in Clustering Problems. In: NIPS 2003 workshop (2003)Google Scholar
  14. 14.
    Fung, G.P.C., Yu, J.X., Lu, H.: Discriminative Category Matching: Efficient Text Classification for Huge Document Collections. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), Maebashi City, Japan, December 09-12 (2002)Google Scholar
  15. 15.
    Lin, D.: Using syntactic dependency as a local context to resolve word sense ambiguity. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, Madrid, July 1997, pp. 64–71 (1997)Google Scholar
  16. 16.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal (August 1995)Google Scholar
  17. 17.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet:Similarity-Measuring the Relatedness of Concepts. AAAI, Menlo Park (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chen Jinxiu
    • 1
  • Ji Donghong
    • 1
  • Tan Chew Lim
    • 2
  • Niu Zhengyu
    • 1
  1. 1.Institute of Infocomm ResearchSingapore
  2. 2.Department of Computer ScienceNational University of SingaporeSingapore

Personalised recommendations