The week's pick
Random Articles
Reseach Article
Pattern Recognition System for Translating the English Sentence into Hindi
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 2 |
| Year of Publication: 2012 |
| Authors: M. M. S. Rauthan, Pritam Singh Negi, H. S. Dhami |
10.5120/4794-7044
|
M. M. S. Rauthan, Pritam Singh Negi, H. S. Dhami . Pattern Recognition System for Translating the English Sentence into Hindi. International Journal of Computer Applications. 39, 2 ( February 2012), 32-37. DOI=10.5120/4794-7044
Abstract
In the present work a model is proposed which deals with specifying the pattern for translating the English sentences into Hindi. Here a Vector Space based translation model has been proposed that transforms a Vector Space by graphical representation of text that addresses the issues of manual, automatic and adaptive strategies by incorporating the selection preferences for word argument positions. Vector Space Model (VSM) represents documents and queries usually as Vectors, Matrices or Tuples. The similarity of the Query Vector and Document Vector is represented as a scalar value. This model constructs a sentence graph for a given sentence and applies structural parsing on this sentence. The quality of a system is measured by considering its usefulness for typical users of the system. The recent development of related techniques stimulates new modeling and estimation methods that are beyond the scope of the traditional approaches. Keywords: Pattern Recognition, Vector Space Model, Mathematical Model.
References
- Aho Alfred V. and Ullman Jeffrey D. (1972), The theory of parsing, translation, and compiling, ACM Classic Books Series.
- Alexandrescu A. and Kirchhoff. K., (2007), Data-Driven Graph Construction for Semi-Supervised Graph-Based Learning in NLP. In HLT.
- Alexandrescu Andrei and Kirchoff katrin, (2009), Graph-based learning for statistical machine translation, Proceedings of Human
- Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics.
- Ali Ola Mohammad, GadAlla Mahmoud, and Abdelwahab Mohammad Said, (2009), Improving Machine Translation using Hybrid Dictionary-Graph Based Word Sense Disambiguation with
- Semantic and Statistical Methods, International Journal of Computer and Electrical Engineering, Vol. 1, No. 5 December, 2009, pp 618-623.
- Amtrupy Jan Willers, (1999), Incremental Speech Translation, Springer.
- Becker Jörg and Kuropka Dominik, (2003), Topic based vector space model; Proceeding of 6th International Conference on Business Information Systems, Poznan, Poland, pages 7-12.
- Erk Katrin and Sebastian Pad´o (2006). A Structured Vector Space Model for Word Meaning in Context; Proceeding of conference on empirical method in natural language (EMNLP-08).
- Franz Josef Och, Christoph Tillman, and Hermann Ney, (1999), Improved Alignment Models for Statistical Machine Translation, Proceedings of EMNLP, pp 20–28.
- G. Salton. (1971), The smart retrieval system. Experiments in Automatic Document Processing, 1971.
- Gudába Milan, Horal Stanislav, Izakovic Ladislav, Kalinová Michaela and Snášel Václav. (2007). Geometrical approach for modeling semantics in linguistics; Proceeding of SYRCODIS Vol-256.
- Hinrich Sch¨utze. (1993), Word space. In Proceedings of the 1993 Conference on Advances in Neural Information Processing Systems, NIPS’93, pages 895–902, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
- Hoede, C., Syntax and semantics, (2004), A comparison of the structuralistic language theory of Ebeling with knowledge graph theory, Memorandum No. 1710, Faculty of Mathematical Sciences, University of twente, Enschede, The Netherlands, ISSN 0169- 2690.
- Huntsville Alabama, (2010), Domain Model Translation Using Graph Transformations, 10th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems (ECBS'03).
- Dan Melamed, (2004), Statistical Machine Translation by Parsing, Proceedings of ACL.
- Jonathan Graehl and Kevin Knight, (2004), Training Tree Transducers, Proceedings of HLT-NAACL.
- Kenji Imamura, Hideo Okuma, Eiichiro Sumita, (2005), Practical
- Approach to Syntax-based Statistical Machine Translation, Proceedings of MTSUMMIT X.
- Kenji Yamada and Kevin Knight, (2001), A Syntax-based Statistical Translation Model, Proceedings of ACL, 2001.
- Lin Dekang, (2004), A path-based transfer model for machine translation, Proceedings of the 20th international conference on Computational Linguistics, Association for Computational Linguistics.
- Peter F. Brown, John Cocke, Stephen Della Pietra, Vincent J. Della Pietra, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, and Paul S. Roossin, (1990), A Statistical Approach to Machine Translation, Computational Linguistics, 16(2), pp 79–85.
- Philip Koehn, Franz Josef Och, and Daniel Marcu, (2003), Statistical Phrase-based Translation, Proceedings of HLT-NAACL, 2003.
- Sahlgren Magnus, (2006), The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high dimensional vector spaces. PhD Dissertation, Department of Linguistics, Stockholm University.
- Zhang L., (2002), Knowledge Graph Theory and Structural Parsing, PhD thesis, University of Twente, Enschede, The Netherlands, ISBN 90-3651835-0.
Index Terms
Keywords