Meaningful Translation and Transliteration for Marathi Language
DOI:
https://doi.org/10.32628/CSEIT217534Keywords:
Natural Language processing, machine learning, Indic languages, information analysis.Abstract
Meaningful translation and transliteration is NP problem in case of languages like Marathi language as there are so many word disambiguation and multiple use and meaning of single word in different context is available. That is why identifying correct informational need and translating text into meaningful information is a tedious and error prone task. Google translate works on machine neuron network and WorldNet is an online reference system works on psycholinguistic theory of human memory. Both approaches are promising tools for language translation. Complete translation of Marathi text to English or English to Marathi also having problem of more complicated meaningless or tedious translation. Proposed algorithm is taking into consideration meaningful translation or transliteration as per user’s informational need. This novel approach consider machine neuron network for meaningful formation of translated sentence and morphological structure for correct translation of word based on ontological analysis of word.
References
- G.V.Gajre, G.Kharate, H. Kulkarni (2014), "Transmuter: An approach to Rule-based English to Marathi Machine Translation" , International Journal of Computer Applications (0975 – 8887) Volume 98 – No.21, July 2014
- R. Mahesh K. Sinha, A Journey from Indian Scripts Processing to Indian Language Processing, IEEE Annals of the History of Computing, Jan-March 2009. pp. 2-25.
- https://ai.googleblog.com/2016/09/a-neural-network-for-machine.html
- Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X. Tensorflow: A system for large-scale machine learning. Tech. rep., Google Brain, 2016. arXiv preprint.
- Brown, P., Cocke, J., Pietra, S. D., Pietra, V. D., Jelinek, F., Mercer, R., and Roossin, P. A statistical approach to language translation. In Proceedings of the 12th Conference on Computational Linguistics - Volume 1 (Stroudsburg, PA, USA, 1988), COLING ’88, Association for Computational Linguistics, pp. 71–76
- Chung, J., Cho, K., and Bengio, Y. A character-level decoder without explicit segmentation for neural machine translation. arXiv preprint arXiv:1603.06147 (2016)
- Gers, F. A., Schmidhuber, J., and Cummins, F. Learning to forget: Continual prediction with LSTM. Neural computation 12, 10 (2000), 2451–2471. 18 Gülçehre, Ç., Ahn, S., Nallapati, R., Zhou, B., and Bengio, Y. Pointing the unknown words. CoRR abs/1603.08148 (2016). 19 Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P. Deep learning with limited numerical precision. CoRR abs/1502.02551 (2015).
- Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi yonghui,schuster,zhifengc,qvl,mnorouzi@google.com
- IndoWordnet, Pushpak Bhattacharyya Bhattacharyya P., Fellbaum C. and Vossen P. (eds.) (2010), Principles, Construction and Application of Multlingual Wordnets, Proceedings of the 5th Global Wordnet Conference, Mumbai, Narosa Publishing House, India.
- Supervised, Semi-Supervised and Unsupervised WSD Approaches: An Overview Lokesh Nandanwar, Kalyani Mamulkar International Journal of Science and Research (IJSR)Open Access | Fully Refereed | Peer Reviewed ISSN: 2319-7064
- R. Mahesh K. Sinha,A Robust POS Tagger Using Multiple Taggers and Lexical Knowledge, ICAI'10 - The 2010 International Conference on Artificial Intelligence, Las Vegas, Nevada, USA, July 12-15, 2010
- R. M. K. Sinha and Anil Thakur, Syntax and Semantics of 'kaa' in Hindi, Proceedings of International Symposium on Machine Translation, NLP and Translation Support System (iSTRANS- 2004), November 17-19, 2004, Tata Mc Graw Hill, New Delhi, pp: 226-229.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.