International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 3 | Issue 5 | Year 2012 | Article Id. IJCTT-V3I5P109 | DOI : https://doi.org/10.14445/22312803/IJCTT-V3I5P109

Particle Swarm Optimization in Transliteration

Dr. Pothula Sujatha

Citation :

Dr. Pothula Sujatha, "Particle Swarm Optimization in Transliteration," International Journal of Computer Trends and Technology (IJCTT), vol. 3, no. 5, pp. 715-718, 2012. Crossref, https://doi.org/10.14445/22312803/IJCTT-V3I5P109

Abstract

Transliteration is the process of transforming a word written in a source language into a word in a target language without the aid of a resource like a bilingual dictionary. This process generates the target language word for a given source language word, but need to find the similarity between source and target words. That is, in order to check how far the generated target word is right equivalent an edit distance calculation is needed between source and target languages words. Presently there was no automated process for finding edit cost between source and target languages words. This work proposes a new Particle Swarm Optimization (PSO) algorithm which is used in the transliteration algorithm process for finding optimal cost between source and target words.

Keywords

Swarm intelligence, particle swarm optimization, transliteration, grapheme, phoneme, hybrid.

References

[1] E. Bonabeau, M. Dorego and G. Theraulaz, “Swarm Intelligence: From natural to artificial systems” Bio-Inspired Computing, 2003.
[2] Julian Togelius , Renzo De Nardi , Alberto Moraglio, “Geometric PSO + GP = Particle Swarm Programming,” In: Proceedings of CEC 2008, pp. 3594–3600.
[3] M. Clerc, “Discrete particle swarm optimization, illustrated by the traveling salesman problem,” New Optimization Techniques in Engineering, Springer, 2004, pp. 219–239.
[4] J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE Transactions on Systems, Man, andCybernetics 5 (1997), 4104–4108.
[5] J. H. Oh and K. S. Choi, “An ensemble of transliteration models for information retrieval,” Information Processing and Management: an International Journal, v.42 n.4, pp. 980-1002, July 2006.
[6] S. Bilac and H. Tanaka, “Improving back-transliteration by combining information sources,” In Proceedings of IJC-NLP, pp. 542–547, 2003.
[7] D. Jurafskyand J. H. Martin, “Speech and Language processing: An introduction to natural language processing,” Computational Linguistics and Speech Recognition, 2007.
[8] S. Garnier, J. Gautrais and G. Theraulaz, “The biological principles of swarm intelligence,” Swarm Intelligence, pp. 3-31, 2007.
[9] Y. Mehdad, “Automatic cost estimation for tree edit distance using particle swarm optimization,” Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 289–292, 2009.
[10] N. A. Jaleel and L. S. Larkey, “Statistical transliteration for englisharabic cross language information retrieval,” In Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA.
[11] B. J. Kang and K. S. Choi, “Automatic transliteration and back- transliteration by decision tree learning,” In: Proc. Of the Second International Conferenceon Language Resources and Evaluation, 2000.
[12] W. H. Lin and H. H. Chen, “Backward machine transliteration by learning phonetic similarity,” In: Proc. of the Sixth Conference on Natural Language Learning, pp. 139–145, 2002.
[13] T. Sherif and G. Kondrak,”Substring based transliteration,” In proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 944-951, 2007.
[14] J. H. Oh and K. S. Choi, “An English-Korean transliteration model   using pronunciation and contextual rules,” In: Proc. Of the 19th International Conference on ComputationalLinguistics, pp. 758–764, 2002.
[15] W. H. Lin and H. H. Chen, “Backward machine transliteration by learning phonetic similarity,” In: Proc. Of the Sixth Conference on Natural Language Learning, pp. 139–145, 2002.
[16] J. H. Oh and K. S. Choi, “An ensemble of transliteration models for information retrieval,” Information Processing and Management: an International Journal, v.42 n.4, pp. 980-1002, July 2006. 
[17] S. Bilac and H. Tanaka, “A hybrid back-transliteration system for Japanese,” In: Proc. Of the 20th International Conference on Computational Linguistics (COLING 2004), pp. 597–603, 2004. 
[18] N. Aswaniand and R. Gaizauskas, “A hybrid approach to align sentences and words in English-Hindi parallel corpora,” In Proceedings of the ACL Workshop on Building and Exploiting Parallel Texts, 2005.
[19] M. G. A. Malik. “Punjabi machine transliteration,” In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pp. 1137–1144, 2006.
[20] H. Surana and A. K. Singh, ” A more discerning and adaptable multilingual transliteration mechanism for Indian languages,” In  Proceedings of International Joint Conference on Natural Language Processing (IJCNLP), 2008, Hyderabad, India.
[21] T. Rama and K. Gali, “Modeling machine transliteration as a phrase based statistical machine translation problem,” In proceedings of the  Named Entities Workshop, ACL–IJCNLP’09, pp. 124-127, August 2009.
[22] VasiNarasimhulu, P. Sujatha, P. Dhavachelvan and M. S. SaleemBasha, “Enhanced Named Entity Transliteration Model Using Machine Learning Algorithm”, International journal of Advancements in Computing Technology Volume 2, Number 3, August, 2010.