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CRF Models for Tamil Part of Speech Tagging and Chunking

  • S. Lakshmana Pandian
  • T. V. Geetha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)

Abstract

Conditional random fields (CRFs) is a framework for building probabilistic models to segment and label sequence data. CRFs offer several advantages over hidden Markov models (HMMs) and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. CRFs also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. In this paper we propose the Language Models developed for Part Of Speech (POS) tagging and chunking using CRFs for Tamil. The Language models are designed based on morphological information. The CRF based POS tagger has an accuracy of about 89.18%, for Tamil and the chunking process performs at an accuracy of 84.25% for the same language.

Keywords

Conditional Random Fields Language Models Part Of Speech (POS) tagging Chunking 

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References

  1. 1.
    Cutting, D., Kupiec, J., Pederson, J., Sibun, P.: A practical part-of-speech tagger. In: Proc. of the 3rd Conference on Applied NLP, pp. 133–140 (1992)Google Scholar
  2. 2.
    Ratnaparkhi, A.: Learning to parse natural language with maximum entropy models. Machine Learning 34 (1999)Google Scholar
  3. 3.
    Sha, F., Pereira, F.: Shallow Parsing with Conditional Random Fields. In: The Proceedings of HLT-NAACL (2003)Google Scholar
  4. 4.
    Freitag, D., McCallum, A.: Information extraction with HMM structures learned by stochastic optimization. In: Proc. AAAI (2000)Google Scholar
  5. 5.
    Bikel, D.M., Schwartz, R.L., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning 34, 211–231 (1999)CrossRefGoogle Scholar
  6. 6.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random _elds: Probabilistic models for segmenting and labeling sequence data. In: Proc. ICML 2001, pp. 282–289 (2001)Google Scholar
  7. 7.
    Koeling, R.: Chunking with Maximum Entropy Models. In: Proceedings of CoNLL 2000, Lisbon, Portugal (2000)Google Scholar
  8. 8.
    Pattabhi, R.K., Rao, T., Vijay Sundar Ram, R., Vijayakrishna, R., Sobha, L.: A Text Chunker and Hybrid POS Tagger for Indian Languages. In: Proceedings of the IJCAI 2007 Workshop On Shallow Parsing for South Asian Languages (SPSAL 2007), Hyderabad, India (2007)Google Scholar
  9. 9.
    Brill, E.: Transformation-based error driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics (1995)Google Scholar
  10. 10.
    Garside, R.: The CLAWS Word-tagging System. In: Garside, R., Leech, G., Sampson, G. (eds.) The Computational Analysis of English: A Corpus-based Approach. Longman, London (1987)Google Scholar
  11. 11.
    Daelemans, W., Zavrel, J., Berck, P., Gillis, S.: MBT: A Memory-Based Part ofSpeech Tagger-Generator. In: Proceedings of the Fourth Workshop on Very Large Corpora, Copenhagen, Denmark, pp. 14–27 (1996)Google Scholar
  12. 12.
    Olde, B.A., Hoener, J., Chipman, P., Graesser, A.C.: The Tutoring Research Group A Connectionist Model for Part of Speech Tagging. In: Proceedings of the 12th International Florida Artificial Intelligence Research Society Conference, Menlo Park, CA, pp. 172–176 (1999)Google Scholar
  13. 13.
    Marques, N., Lopes, J.G.: Using Neural Nets for Portuguese Part-of-Speech Tagging. In: Proceedings of the Fifth International Conference on The Cognitive Science of Natural Language Processing, Dublin City University (1996)Google Scholar
  14. 14.
    Ratnaparkhi, A.: Maximum Entropy Model For Natural Language Ambiguity Resolution, Dissertation in Computer and Information Science, University Of Pennslyvania (1998)Google Scholar
  15. 15.
    Punyakanok, V., Roth, D.: The use of classifiers in sequential inference. In: NIPS, vol. 13, pp. 995–1001. MIT Press, Cambridge (2001)Google Scholar
  16. 16.
    Abney, S., Schapire, R.E., Singer, Y.: Boosting applied totagging and PP attachment. In: Proc. EMNLP-VLC, NewBrunswick, New Jersey, ACL (1999)Google Scholar
  17. 17.
    Kudo, T., Matsumoto, Y.: Chunking with. support vector machines. In: Proceedings of NAACL, pp. 192–199 (2001)Google Scholar
  18. 18.
    CRF++: Yet Another Toolkit, http://chasen.org/~taku/software/CRF++
  19. 19.
    Lafferty, J.: Andrew McCallum and Fernando Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proc. of the International Conference on Machine Learning (ICML) (2001)Google Scholar
  20. 20.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings of the IEEE. IEEE, Los Alamitos (1989) IEEE Log Number 8825949Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. Lakshmana Pandian
    • 1
  • T. V. Geetha
    • 1
  1. 1.Department of Computer Science and EngineeringAnna UniversityChennaiIndia

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