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Approaches for Improving Hindi to English Machine Translation System
Objectives: To provide approaches for effective Hindi-to-English Machine Translation (MT) that can be helpful in inexpensive and ease implementation of and MT systems. Methods/Statistical Analysis: Structure of the Hindi and English languages have been studied thoroughly. The possible steps towards the Natural languages have also been studied. The methods, rules, approaches, tools, resources etc. related to MT have been discussed in detail. Findings: MT is an idea for automatic translation of a language. India is the country with full of diversity in culture and languages. More than 20 regional languages are spoken along with several dialects. Hindi is a widely spoken language in all the states of country. A lot of literature, poetries and valuable texts are available in Hindi which gives opportunities to retranslate into English. However, new generation is learning English rapidly and also showing keenness to learn it in simplified lucid manner. Several efforts have been made in this direction. A large number of approaches and solutions exist for MT still there is a huge scope. The paper addresses the challenges of MT and solution efforts made in this direction. This motivates researchers to implement new Hindi-to-English Machine translation systems. Application/Improvements: Efficient, inexpensive and ease translation for available Hindi literature, poetries and other valuable texts into English. Children can easily learn the culture through the poetries and literatures hence the Machine Translation of these will bring wonderful impact.
English Language, Hindi Language, Machine Translation, Translation-Rules and Translation Approaches.
- Naskar S, Bandyopadhyay S. Use of Machine Translation in India: Current Status. Thailand: Phuket: Proceedings of MT SUMMIT X. 2005 September; p. 465-70.
- Garje GV, Kharate GK. Survey of Machine Translation Systems in India. International Journal on Natural Language Computing (IJNLC). 2013 October; 2(4):47-67. Crossref
- Latha RN, David PS. Machine Translation Systems for Indian Languages. International Journal of Computer Applications (IJCA). 2012 February; 39(1):25-31.
- Jurafsky D, Martin JH. Speech and language processing. 2nd edn. Pearson Education India. 2002.
- Rao D. Machine Translation in India. Bangalore: SCALLA 2001 Conference: A Brief Survey. 2001; p. 1-6.
- Dungarwal P, Chatterjee R, Mishra A, Kunchukuttan A, Shah R, Bhattacharyya P. The IIT Bombay Hindi⇔ English Translation System at WMT 2014. Association of Computational Linguistic (ACL). 2014; p. 1-7.
- Agirre E, Edmonds PG. Word sense disambiguation. Algorithms and applications. Springer Science and Business Media. 2007; 33(1):255-58.
- Das A, Sarkar S. ICON.201: 3Word Sense Disambiguation in Bengali applied to Bengali-Hindi Machine Translation. 2013; p. 1-10.
- Bandyopadhyay S. Teaching MT - An Indian Perspective. UK: Manchester: Proceedings of the 6th EAMT Workshop on Teaching Machine Translation. 2002; p.13-22.
- Badodekar S. Translation resources, services and tools for Indian languages. Computer Science and Engineering Department. Indian Institute of Technology, Mumbai, 2003. Date accessed: 21/03/2016: Available from: http:// www.cfilt.iitb.ac.in/Translation-survey/survey.pdf.
- Shallu S, Gupta V. A Survey of Word-sense Disambiguation Effective Techniques and Methods for Indian Languages. Journal of Emerging Technologies in Web Intelligence. 2013 November; 5(4):354-60. Crossref
- Tripathi S, Sarkhel JK. Approaches to machine translation. Annals of library and information studies. 2010; 57(1):38893.
- Sanyal S, Borgohain R. Machine Translation Systems in India. 2013; p. 5. Available from: arXiv preprint arXiv.1304.7728.
- Antony PJ. Machine translation approaches and survey for Indian languages. International journal of Computational Linguistics and Chinese Language Processing. 2013 March; 18(1):47-78.
- Godase A, Govilkar S. Machine Translation Development for Indian Languages and its Approaches. Date accessed: 16/06/2016: Available from: http://airccse.org/journal/ ijnlc/papers/4215ijnlc05.pdf.
- Goyal V, Lehal GS. Advances in Machine Translation Systems. Language in India. 2009; 9(11):1-13.
- Dwivedi SK, Sukhadeve PP. Machine Translation System in Indian perspectives. Journal of computer science. 2010; 6(10):1111-16. Crossref
- Bhattacharyya P. Natural language processing: A perspective from computation in presence of ambiguity, resource constraint and multilinguality. CSI Journal of Computing. 2012; 1(2):1-13.
- Sinha RMK, Sivaraman K, Agrawal A, Jain R, Srivastava R, Jain A. ANGLABHARTI, a multilingual machine aided translation project on translation from English to Indian languages. IEEE International Conference on System MAN and Cybernatics. 1995; 2(1):1609-14. Crossref
- Darbari H. Computer-assisted translation system–an Indian perspective. Machine Translation Summit VII. 1999 September; p. 80-85.
- Bharati A, Chaitanya V, Kulkarni AP, Sangal R, Rao GU. Anusaaraka, overcoming the language barrier in India. arXiv preprint cs/0308018. 2003; p. 1-19.
- Dave S, Parikh J, Bhattacharyya P. Interlingua-based English-Hindi machine translation and language divergence. Machine Translation. 2001; 16(4):251-304. Crossref
- Sinha RMK, Jain A. AnglaHindi, an English to Hindi machine-aided translation system. USA: New Orleans: MT Summit IX. 2003; p. 494-97.
- Ananthakrishnan R, Kavitha M, Jayprasad JH, Shekhar RS, Bade SM. MaTra. A practical approach to fully-automatic indicative English-Hindi machine translation. Symposium on Modeling and Shallow Parsing of Indian Languages (MSPIL’06). 2006; p. 1-8.
- Bhattacharyya P. Machine Translation. USA: CRC Press: Taylor and Francis Group. 2015.
- Miller G. A. WordNet, a lexical database for English. Communications of the ACM. 1995 November; 38(11):3941. Crossref
- Bhattacharyya P. IndoWordNet, Proceedings of LREC-10. 2010; p. 1-10.
- Dwivedi SK, Rastogi P. Critical analysis of WSD algorithms. Proceedings of the International Conference on Advances in Computing. ACM: Communication and Control. 2009 January; 3:62-7. Crossref
- Navigli R, Lapata M. An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010 April; 32(4):678-92. Crossref PMid:20224123
- Novak JD, Canas AJ. The theory underlying concept maps and how to construct them. Florida Institute for Human and Machine Cognition. 2006; 1:1-6.
- Ca-as AJ, Valerio A, Lalinde-Pulido J, Carvalho M, Arguedas M. Using WordNet for Word Sense Disambiguation to support Concept Map construction. Springer Berlin Heidelberg: String Processing and Information Retrieval. January 2003; p. 350-59.
- Liu Z, Zhang X, Kato J. Research on Chinese-Japanese Sign Language Translation System. IEEE Fifth International Conference on Frontier of Computer Science and Technology (FCST). 2010 August; p. 640-45. Crossref
- Sinha RMK, Thakur A. Machine Translation of bi-lingual Hindi-English (Hinglish) text. Thailand: Phuket : Tenth Machine Translation summit. 2006; p. 149-56.
- Ananthakrishnan R, Bhattacharyya P, Sasikumar M, Shah RM. Some issues in automatic evaluation of English-Hindi MT, more blues for BLUE. ICON-2017. 2007; p. 1-8.
- Sawant DG. Translation literature in India, 2012. Date accessed: 18/07/2016: Available from: https://www. researchgate.net/publication/230814146.
- Hariyanto S. Problems in Translating Poetry. Date accessed: 19/09/2016: Available from: http://www.translationdirectory. com/article640.htm.
- Ali OM, GadAlla M, Abdelwahab MS. Word Sense Disambiguation in Machine Translation using Monolingual Corpus. Proceedings of the Eighth Conference on Language Engineering. Egypt: Cairo: Ain Shams University. 2008; p.141-51.
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