Multiobjective Optimization Approach for Named Entity Recognition

  • Asif Ekbal
  • Sriparna Saha
  • Christoph S. Garbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6230)

Abstract

In this paper, we propose a multiobjective optimization (MOO) based technique to determine the appropriate weight of voting for each class in each classifier for Named Entity Recognition (NER). Our underlying assumption is that reliability of predictions of each classifier differs among the various named entity (NE) classes. Thus, it is necessary to quantify the amount of voting for each class in a particular classifier. We use Maximum Entropy (ME) as the base to generate a number of classifiers depending upon the various feature representations. The proposed algorithm is evaluated for a resource-constrained language like Bengali that yield the overall recall, precision and F-measure values of 79.98%, 82.24% and 81.10%, respectively. Experiments also show that the classifier ensemble identified by the proposed multiobjective based technique outperforms all the individual classifiers, three different conventional baseline ensembles and an existing single objective optimization based approach.

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References

  1. 1.
    Cunningham, H.: GATE, a General Architecture for Text Engineering. Computers and the Humanities 36, 223–254 (2002)CrossRefGoogle Scholar
  2. 2.
    Babych, B., Hartley, A.: Improving Machine Translation Quality with Automatic Named Entity Recognition. In: Proceedings of EAMT/EACL 2003 Workshop on MT and other Language Technology Tools, pp. 1–8 (2003)Google Scholar
  3. 3.
    Moldovan, D., Harabagiu, S., Girju, R., Morarescu, P., Lacatusu, F., Novischi, A., Badulescu, A., Bolohan, O.: LCC Tools for Question Answering. In: Text REtrieval Conference, TREC 2002 (2002)Google Scholar
  4. 4.
    Nobata, C., Sekine, S., Isahara, H., Grishman, R.: Summarization System Integrated with Named Entity Tagging and IE Pattern Discovery. In: Proceedings of Third International Conference on Language Resources and Evaluation (LREC 2002), Spain (2002)Google Scholar
  5. 5.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)MATHGoogle Scholar
  6. 6.
    Ekbal, A., Saha, S.: Weighted Vote Based Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition. In: Proceedings of 15th International Conference on Applications of Natural Language to Information Systems (NLDB 2010), Cardiff, Wales, UK, pp. 256–267 (2010)Google Scholar
  7. 7.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Ltd., England (2001)MATHGoogle Scholar
  8. 8.
    Ekbal, A., Bandyopadhyay, S.: A Web-based Bengali News Corpus for Named Entity Recognition. Language Resources and Evaluation Journal 42(2), 173–182 (2008)CrossRefGoogle Scholar
  9. 9.
    Florian, R., Ittycheriah, A., Jing, H., Zhang, T.: Named Entity Recognition through Classifier Combination. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003 (2003)Google Scholar
  10. 10.
    Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 Shared Task: Language Independent Named Entity Recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp. 142–147 (2003)Google Scholar
  11. 11.
    Ekbal, A., Bandyopadhyay, S.: Voted NER System using Appropriate Unlabeled Data. In: Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009), ACL-IJCNLP 2009, pp. 202–210 (2009)Google Scholar
  12. 12.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 181–197 (2002)CrossRefGoogle Scholar
  13. 13.
    Ekbal, A., Bandyopadhyay, S.: Web-based Bengali News Corpus for Lexicon Development and POS Tagging. POLIBITS 37, 20–29 (2008)Google Scholar
  14. 14.
    Anderson, T.W., Scolve, S.: Introduction to the Statistical Analysis of Data. Houghton Mifflin (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Asif Ekbal
    • 1
  • Sriparna Saha
    • 2
  • Christoph S. Garbe
    • 2
  1. 1.Department of Computational LinguisticsHeidelberg UniversityGermany
  2. 2.Image Processing and Modeling, Interdisciplinary Center for Scientific Computing (IWR)Heidelberg UniversityHeidelbergGermany

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