A Novel Approach for Odia Part of Speech Tagging Using Artificial Neural Network

  • Bishwa Ranjan Das
  • Srikanta Patnaik
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


This paper presents a challenging task for POS Tagging using Artificial Neural Network for Odia language. Neural Network is used for Odia POS Tagging. A Single Neural Network based POS Tagger with fixed length of context chosen empirically is presented first. Then a multiple neuron tagger which consists of multiple single-neuron taggers with fixed but different lengths of contexts is presented. Multi-neuron tagger performs tagging by voting on the output of all single neuron tagger. The experiments carried out are discussed, Neural Network for efficient recognition where the errors were corrected through forward propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers, i.e. the input layer, the output layer and the middle layer or hidden layers. Neural networks are one of the most efficient techniques for identified the correct data. A small labeled training set is provided; a HMM based approach does not yield very good result. So in this work, morphological analyzer is used to improve the performance of the tagger. This tagger has an accuracy of about 81% on the test data provided.


Single layer feed forward Multi layer feed forward Hidden layer Panini grammar 


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  1. 1.
    Parikh, A.: Part-of-speech Tagging using neural network. In: Proceedings of ICON 2009: 7th International Conference on Natural Language Processing, Report No: IIIT/TR/2009/232 (2009)Google Scholar
  2. 2.
    Ray, P.R., Harish, V., Sarkar, S., Basu, A.: Part of speech Tagging and Local word Grouping Techniques for Natural Language parsing in Hindi. In: Proceedings of the 1st International Conference on Natural Language Processing (ICON 2003), Mysore (2003)Google Scholar
  3. 3.
    Schmid, H.: Part-Of- Speech Tagging with Neural Networks. In: COLING 1994 Proceedings of the 15th Conference on Computational Linguistics, vol. 1, pp. 172–176 (1994)Google Scholar
  4. 4.
    Jena, I., Haudhury, S.C., Chaudhry, H., Sharma, D.M.: Developing Oriya Morphological Analyzer Using Lt-toolbox. Information Systems for Indian Languages Communications in Computer and Information Science 139, 124–129 (2011)CrossRefGoogle Scholar
  5. 5.
    Singh, S., Gupta, K., Shrivastava, M., Bhattacharyya, P.: Morphological richness offsets resource demand – experiences in constructing a pos tagger for Hindi. In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, Sydney, Australia, pp. 779–786 (2006)Google Scholar
  6. 6.
    Ekbal, A., Mondal, S., Bandyopadhyay, S.: POS Tagging using HMM and Rule-based Chunking. In: Proceedings of SPSAL 2007, IJCAI 2007, pp. 25–28 (2007)Google Scholar
  7. 7.
    Ekbal, A., Haque, R., Bandyopadhyay, S.: Maximum Entropy Based Bengali Part of Speech Tagging. Advances in Natural Language Processing and Applications, Research in Computing Science (RCS) Journal (33), 67–78 (2008)Google Scholar
  8. 8.
    Ekbal, A., Bandyopadhyay, S.: Part of Speech Tagging in Bengali using Support Vector Machine. In: Proceedings of the International Conference on Information Technology (ICIT 2008), pp. 106–111. IEEE (2008)Google Scholar
  9. 9.
    Ekbal, A., Haque, R., Bandyopadhyay, S.: Bengali Part of Speech Tagging using Conditional Random Field. In: Proceedings of the 7th International Symposium on Natural Language Processing (SNLP 2007), Thailand, pp. 131–136 (2007)Google Scholar
  10. 10.
    Ekbal, A., Hasanuzzaman, M., Bandyopadhyay, S.: Voted Approach for Part of Speech Tagging in Bengali. In: Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation (PACLIC 2009), Hong Kong, December 3-5, pp. 120–129 (2009)Google Scholar
  11. 11.
    Dhanalakshmi, V., Anandkumar, M., Shivapratap, G., Soman, K.P., Rajendran, S.: Tamil POS Tagging using Linear Programming. International Journal of Recent Trendsin Engineering 1(2), 166–169 (2009)Google Scholar
  12. 12.
    Sreeganesh, T.: Telugu Parts of Speech Tagging in WSD. Language of India 6 (August 8, 2006)Google Scholar
  13. 13.
    Patel, C., Gali, K.: Part-Of-Speech Tagging for Gujarati Using Conditional Random Fields. In: Proceedings of the IJCNLP 2008 Workshop on NLP for Less Privileged Languages, Hyderabad, India, pp. 117–122 (2008)Google Scholar
  14. 14.
    Singh, T.D., Bandyopadhyay, S.: Morphology DrivenManipuri POS Tagger. In: Proceedings of the IJCNLP 2008 Workshop on NLP for Less Privileged Languages, Hyderabad, Hyderabad, India, pp. 91–98 (2008)Google Scholar
  15. 15.
    Saharia, N., Das, D., Sharma, U., Kalita, J.: Part of Speech Tagger for Assamese Text. In: Proceedings of the ACL IJCNLP 2009 Conference Short Papers, Suntec, Singapore, pp. 33–36 (2009)Google Scholar
  16. 16.
    Bharati, A., Chaitanya, V., Sangal, R.: Natural Language Processing A Paninian Perspective. In: Department of Computer Science and Engineering Indian Institute of Technology Kanpur, With contributions from K.V. Ramakrishnamacharyulu Rashtriya Sanskrit Vidyapeetha, Tirupati. Prentice-Hall, India (1994)Google Scholar
  17. 17.
    Data Mining, Concepts & Techniques, Jiawei Han and Micheline Kamber, 4th edn. Elsevier (2008)Google Scholar
  18. 18.
    Neural Networks, A.: Comprehensive Foundation, Simon Haykin. PHI (1998)Google Scholar
  19. 19.
    Machine Learning of Natural Language, Walter Daeleman, CNTS Language Technology Group Department of Linguistics, University of Antwerp, Belgium,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and Information Technology, Institute of Technical Education and ResearchSiksha ’O’Anusandhan UniversityBhubaneswarIndia

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