Advertisement

Feature Selection in Anaphora Resolution for Bengali: A Multiobjective Approach

  • Utpal Kumar SikdarEmail author
  • Asif Ekbal
  • Sriparna Saha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)

Abstract

In this paper we propose a feature selection technique for anaphora resolution for a resource-poor language like Bengali. The technique is grounded on the principle of differential evolution (DE) based multiobjective optimization (MOO). For this we explore adapting BART, a state-of-the-art anaphora resolution system, which is originally designed for English. There does not exist any globally accepted metric for measuring the performance of anaphora resolution, and each of muc, B3, ceaf, Blanc exhibits significantly different behaviours. System optimized with respect to one metric often tend to perform poorly with respect to the others, and therefore comparing the performance between the different systems becomes quite difficult. In our work we determine the most relevant set of features that best optimize all the metrics. Evaluation results yield the overall average F-measure values of 66.70%, 59.70%, 51.56%, 33.08%, 72.75% for MUC, B3, CEAFM, CEAFE and BLANC, respectively.

Keywords

Feature Selection Differential Evolution Pareto Optimal Front Person Pronoun Single Objective Optimization 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Soon, W.M., Chung, D., Lim, D.C.Y., Lim, Y., Ng, H.T.: A machine learning approach to coreference resolution of noun phrases (2001)Google Scholar
  2. 2.
    Ng, V., Cardie, C.: Improving machine learning approaches to coreference resolution. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 104–111 (2002)Google Scholar
  3. 3.
    Walker, C., Strassel, S., Medero, J., Maeda, K.: Ace 2005 multilingual training corpus: Ldc2006t06 philadelphia penn.: Linguistic data consortium (2006)Google Scholar
  4. 4.
    Weischedel, R., Pradhan, S., Ramshaw, L., Palmer, M., Xue, N., Marcus, M., Taylor, A., Greenberg, C., Hovy, E., Belvin, R., Houston, A.: Ontonotes release 2.0:ldc2008t04 philadelphia penn.: Linguistic data consortium (2008)Google Scholar
  5. 5.
    Versley, Y., Ponzetto, S.P., Poesio, M., Eidelman, V., Jern, A., Smith, J., Yang, X., Moschitti, A.: Bart: A modular toolkit for coreference resolution. In: HLT-Demonstrations 2008 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies, pp. 9–12 (2008)Google Scholar
  6. 6.
    Sobha, L., Patnaik, B.N.: Vasisth: An anaphora resolution system for indian languages. In: Proceedings Artificial and Computational Intelligence for Decision, Control and Automation in Engineering and Industrial Applications (ACIDCA), Monastir, Tunisia (2000)Google Scholar
  7. 7.
    Agarwal, S., Srivastava, M., Agarwal, P., Sanyal, R.: Anaphora resolution in hindi documents. In: Proceedings of Natural Language Processing and Knowledge Engineering (IEEE NLP-KE), Beijing, China (2007)Google Scholar
  8. 8.
    Uppalapu, B., Sharma, D.: Pronoun resolution for hindi. In: Proceedings of DAARC (2009)Google Scholar
  9. 9.
    Devi, S.L., Ram, V.S., Rao, P.R.: A generic anaphora resolution engine for indian languages. In: Proceedings of COLING 2014, pp. 1824–1833 (2014)Google Scholar
  10. 10.
    Sikdar, U., Ekbal, A., Saha, S., Uryupina, O., Poesio, M.: Adapting a state-of-the-art anaphora resolution system for resource-poor language. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 815–821. Asian Federation of Natural Language Processing (2013)Google Scholar
  11. 11.
    Senapati, A., Garain, U.: Guitar-based pronominal anaphora resolution in bengali. In: Proceedings of ACL, Sofia, Bulgaria (2013)Google Scholar
  12. 12.
    Sikdar, U.K., Ekbal, A., Saha, S., Uryupina, O., Poesio, M.: Differential evolution-based feature selection technique for anaphora resolution. Soft Computing, 1–13 (2014)Google Scholar
  13. 13.
    Uryupina, O.: Knowledge Acquisition for Coreference Resolution. PhD thesis, University of the Saarland (2007)Google Scholar
  14. 14.
    Hoste, V.: Optimization Issues in Machine Learning of Coreference Resolution. PhD thesis, Antwerp University (2005)Google Scholar
  15. 15.
    Saha, S., Ekbal, A., Uryupina, O., Poesio, M.: Single and multi-objective optimization for feature selection in anaphora resolution. In: Proceedings of the fifth International Joint Conference in Natural Langauge Processing (IJCNLP 2011), pp. 93–101 (2011)Google Scholar
  16. 16.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: ICML, pp. 282–289 (2001)Google Scholar
  17. 17.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  18. 18.
    Quinlan, J.R.: Programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  19. 19.
    Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. of Global Optimization 11(4), 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Anderson, T.W., Scolve, S.: Introduction to the Statistical Analysis of Data. Houghton Mifflin (1978)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Utpal Kumar Sikdar
    • 1
    Email author
  • Asif Ekbal
    • 1
  • Sriparna Saha
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyPatnaIndia

Personalised recommendations