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Effect of Overt Pronoun Resolution in Topic Tracking

  • Fumiyo Fukumoto
  • Yoshimi Suzuki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6562)

Abstract

This article focuses on overt pronouns which are related to a topic and an event in news stories, and studies issues on the effect of their resolution in topic tracking. The antecedent of the pronoun is identified by using three linguistic features, morphological, syntactic and semantic knowledge. The morphological cues are part-of-speech information including named entities. Syntactic and semantic information is verbs and their subcategorization frames with selectional preferences. They are derived from the WordNet and VerbNet. The results on the TDT3 English show the usefulness of the overt pronoun resolution, especially for a small number of positive training data.

Keywords

Noun Phrase Cosine Similarity Computational Linguistics Selectional Preference Semantic Class 
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.

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References

  1. 1.
    Kennedy, C., Branimir, N.: Anaphora for Everyone: Pronominal Anaphora Resolution without a Parser. In: Proc. of the 16th International Conference on Computational Linguistics, pp. 113–118 (1996)Google Scholar
  2. 2.
    Harman, D.: Overview of the forth Text REtrieval Conference (TREC4). In: Proc. of the 4th Text REtrieval Conference (1996)Google Scholar
  3. 3.
    Lin, D.: Automatic Retrieval and Clustering of Similar Words. In: Proc. of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pp. 768–774 (1998)Google Scholar
  4. 4.
    Oard, D.W.: Topic Tracking with the PRISE Information Retrieval System. In: Proc. of DARPA Workshop (1999)Google Scholar
  5. 5.
    Schmid, H.: Improvements in Part-of-Speech Tagging with an Application to German. In: Proc. of the EACL SIGDAT Workshop (1995)Google Scholar
  6. 6.
    Allan, J.: Incremental Relevance Feedback for Information Filtering. In: Proc. of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11–22 (1996)Google Scholar
  7. 7.
    Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic Detection and Tracking Pilot Study Final Report. In: Proc. of DARPA Workshop (1998)Google Scholar
  8. 8.
    Allan, J., Papka, R., Lavrenko, V.: On-line New Event Detection and Tracking. In: Proc. of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45 (1998)Google Scholar
  9. 9.
    Allan, J., Lavrenko, V., Connell, M.E.: A Month to Topic Detection and Tracking in Hindi. ACM Trans. on Asian Language Information Processing (TALIP) 2(2), 85–100 (2003)CrossRefGoogle Scholar
  10. 10.
    Carbonell, J., Yang, Y., Lafferty, J., Brown, R.D., Pierce, T., Liu, X.: CMU Report on TDT-2: Segmentation, Detection and Tracking. In: Proc. of DARPA Workshop (1999)Google Scholar
  11. 11.
    Fiscus, J.: Overview of the TDT 2001 Evaluation and Results. In: Workshop in TDT 2001 (2001)Google Scholar
  12. 12.
    Fiscus, J.G., Doddington, G.R.: Topic Detection and Tracking Evaluation Overview. In: Allan, J. (ed.) Topic Detection and Tracking. Kluwer Academic Publisher, Dordrecht (2002)Google Scholar
  13. 13.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In: Proc. of the 43nd Annual Meeting of the Association for Computational Linguistics, pp. 363–370 (2005)Google Scholar
  14. 14.
    Markert, K., Nissim, M.: Comparing Knowledge Sources for Nominal Anaphora Resolution. Computational Linguistics 31(3), 367–401 (2005)CrossRefGoogle Scholar
  15. 15.
    Larkey, L.S., Feng, F., Connell, M., Lavernko, V.: Language-specific Model in Multilingual Topic Tracking. In: Proc. of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 402–409 (2004)Google Scholar
  16. 16.
    Connell, M., Feng, A., Kumaran, G., Raghavan, H., Shah, C., Allan, J.: UMASS at TDT 2004. In: Proc. of DARPA Workshop (2004)Google Scholar
  17. 17.
    Franz, M., McCarley, J.S.: Unsupervised and Supervised Clustering for Topic Tracking. In: Proc. of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 310–317 (2001)Google Scholar
  18. 18.
    Yu, M.Q., Luo, W.H., Zhou, Z.T., Bai, S.: ICT’s Approaches to HTD and Tracking at TDT2004. In: Proc. of the Topic Detection and Tracking Workshop (2004)Google Scholar
  19. 19.
    Strube, M., Stefan, R., Christoph, M.: The Influence of Minimum Edit Distance on Reference Resolution. In: Proc. of the 2002 Conference on Empirical Methods in Natural Language Processing, pp. 312–319 (2002)Google Scholar
  20. 20.
    Belkin, N.J., Croft, W.B.: Information filtering and Information Retrieval: Two sides of the same coin? Communications of the ACM 35(2), 29–38 (1992)CrossRefGoogle Scholar
  21. 21.
    Mitkov, R.: Robust Pronoun Resolution with Limited Knowledge. In: Proc. of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pp. 869–875 (1998)Google Scholar
  22. 22.
    Elsayed, T., Oard, D.W., Doermann, D.: TDT-2004: Adaptive Topic Tracking at Maryland. In: Proc. of DARPA Workshop (2004)Google Scholar
  23. 23.
    Zhang, Y., Callan, J.: CMU DIR Supervised Tracking Report. In: Proc. of DARPA Workshop (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fumiyo Fukumoto
    • 1
  • Yoshimi Suzuki
    • 1
  1. 1.Interdisciplinary Graduate School of Medicine and EngineeringUniv. of YamanashiKofuJapan

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