Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Implementation of Enhanced Parts-of-Speech Based Rules for English to Telugu Machine Translation
A. P. Siva Kumar, A.Govardhan, P. Premchand
Pages - 1 - 9     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 2   Issue - 1    |    Publication Date - July / August 2011  Table of Contents
POS-Based Reordering, English to Telugu CLIR, BLEU
Words of a sentence will not follow same ordering in different languages. This paper proposes certain Parts-of-Speech (POS) based rules for reordering the given English sentence to get translation in Telugu. The added rules for adverbs, exceptional conjunctions in addition to improved handling of inflections enable the system to achieve more accurate translation. The proposed rules along with existing system gave a score of 0.6190 with BLEU evaluation metric while translating sentences from English to Telugu. This paper deals with simple form of sentences in a better way.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 Scribd
4 SlideShare
5 PdfSR
1 R.Gangadharaiah & N. Balakrishnan, “Application of Linguistic Rules to Generalized Example Based Machine Translation for Indian Languages”, Proceedings of the First National Symposium on Modeling and Shallow Parsing of Indian Languages, India, 2006
2 Mustafa Abusalah, John Tait & Michael Oakes, “Literature Review of Cross Language Information Retrieval”, World Academy of Science, Engineering and Technology, 2005.
3 P.Kishore, Salim Roukas, Todd ward & Wei-Jing Zhu, “BLEU: a Method for Automatic Evaluation of Machine Translation”, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pp. 311-318, 2002.
4 Maja Popovic & Hermann Ney, “POS-based Word Reorderings for Statistical Machine Translation”, in Proceedings of the Fifth International conference on Language Resources and Evaluation, 2006.
5 Anne R. Diekema, “Translation Events in Cross-Language Information Retrieval: Lexical Ambiguity, Lexical Holes, Vocabulary Mismatch, and Correct Translation”, Dissertation at School of Information Studies, Syracuse University, 2003.
6 Sethuramalingam S, “Effective Query Translation Techniques for Cross-Language Information Retrieval”, MS Thesis submitted at IIIT Hyderabad, India, 2009.
7 Sudip Naskar & Sivaji Bandyopadhyay, “Use of Machine Translation in India: Current Status”,AAMT J., 36:25-31, 2004.
8 Sanjay Kumar Dwivedi and Pramod Premdas Sukhdeve, “Machine Translation System in Indian Perspectives”, Journal of Computer Science 6 (10): 1082-1087, 2010.
9 Shu Cai, Yajuan L & Qun Liu, “Improved Reordering Rules for Hierarchical Phrase-based Translation”, International Conference on Asian Language Processing, 2009.
10 ZHANG Xiao-fei, HUANG He-yan & ZHANG Ke-liang, “Cross-Language Information Retrieval Based on Weight Computation of Query Keywords Translation”, Intelligent Computing and Intelligent Systems, 2009 IEEE International Conference, 2009.
11 Parts-Of-Speech tagger tool – http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger.
Mr. A. P. Siva Kumar
JNT University Anantapur - India
Dr. A.Govardhan
JNT University Hyderabad - India
Dr. P. Premchand
Osmania University - India