Abstract
In this paper we present an English-to-Bengali back transliteration system that can be used to transliterate Bengali texts written in Romanized English, back to its original script. Our proposed system uses a bilingual parallel corpus of English-Bengali transliterated word pairs and applies both the orthographic as well as phonetic information to two different computational models namely, the joint source channel model and the trigram model, to automatically identify, extract and learning of transliteration unit (TU) pairs from both the source and target language words. Finally, the system predicts the top 10 best possible outcome of the given input text. We further extend our work to make the target word prediction module more robust. This is done by the phonological analysis of the generated target sentence. Both the models have been evaluated with a set of 2000 Romanized Bengali test words. Our initial evaluation results clearly shows that the joint source channel model performs much better than the trigram model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ekbal, A., Naskar, S., Bandyopadhyay, S.: A modified joint source-channel model for transliteration. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 191–198 (2006)
Zhang, M., Li, H., Kumaran, A., Liu, M.: Report of NEWS 2012 machine transliteration shared task. In: Proceedings of the 4th Named Entity Workshop, pp. 10–20 (2012)
Schwartz, R., Chow, Y.L.: The N-best algorithm: An efficient and Exact procedure for finding the N most likely sentence hypothesi. In: Proceedings of ICASSP 1990, Albuquerque, pp. 81–84 (1990)
Knight, K., Graehl, J.: Machine Transliteration. Computational Linguistics 24(4), 599–612 (1998)
Ehara, Y., Tanaka-Ishii, K.: Multilingual text entry using automatic language detection. In: Proceedings of IJCNLP, pp. 441–448 (2008)
Lee, C.-J., Chang, J.S.: Acquisition of English-Chinese Transliteration Word Pairs from Parallel-Aligned Texts using a Statistical Machine (2003)
Translation Model. In: Proceedings of HLT-NAACL Workshop: Building and Using parallel Texts Data Driven Machine Translation and Beyond, Edmonton, pp. 96–103 (2003)
Li, H., Kumaran, A., Zhang, M., Pervouchine, V.: Report of NEWS 2009 Machine Transliteration Shared Task. In: Proceedings of ACL-IJCNLP 2009 Named Entities Workshop, pp. 1–18 (2009)
Goto, I., Kato, N., Uratani, N., Ehara, T.: Transliteration considering Context Information based on the Maximum Entropy Method. In: Proceeding of the MT-Summit IX, New Orleans, USA, pp. 125–132 (2003)
Li, H., Min, Z., Jian, S.: A Joint Source-Channel Model for Machine Transliteration. In: Proceedings of the 42nd Annual Meeting of the ACL (ACL 2004), Barcelona, Spain, pp. 159–166 (2004)
Lee, J.S., Choi, S.: English to Korean statistical transliteration for information retrieval. Computer Processing of Oriental Languages 12(1), 17–37 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Dasgupta, T., Sinha, M., Basu, A. (2013). A Joint Source Channel Model for the English to Bengali Back Transliteration. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-03844-5_73
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
eBook Packages: Computer ScienceComputer Science (R0)