An Approach for Improving Accuracy of Machine Translation using WSD and GIZA

Authors

  • SG Rawat Dept. of IT, G.H.Raisoni College of Engineering, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, India
  • MB Chandak Dept. of CSE, Shri Ramdeobaba College of Engg. and Management, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, India
  • NA Chavan Dept. of IT, G.H.Raisoni College of Engineering, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, India

DOI:

https://doi.org/10.26438/ijcse/v5i10.256259

Keywords:

WSD, Machine Translation, Corpus, Supervised, Unsupervised

Abstract

Word Sense Disambiguation (WSD) is a challenging problem of Natural Language Processing (NLP). Though there are lots of algorithms for WSD available, still little work is carried out for choosing optimal algorithm for that. The job of word sense disambiguation is to decide the accurate meaning of an ambiguous term in a particular circumstance. When WSD is used in machine translation, an accurate translation in the resultant linguistic must be determined for an ambiguous term entry in the original language. Therefore Word Sense Disambiguation remains one of the most common real life problems that are associated to natural language processing which needs to be resolved efficiently.Unsupervised techniques use online dictionary for learning, and supervised techniques use manual learning sets. As there are some advantages and disadvantages of supervised learning and unsupervised learning, aim of this paper is to disambiguate the ambiguous word by using the hybrid approach for WSD. We have made use of parallel corpus and aligned the text by using GIZA.

References

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i10.256259
Published: 2025-11-12

How to Cite

[1]
S. Rawat, M. Chandak, and N. Chavan, “An Approach for Improving Accuracy of Machine Translation using WSD and GIZA”, Int. J. Comp. Sci. Eng., vol. 5, no. 10, pp. 256–259, Nov. 2025.

Issue

Vol. 5 No. 10 (2017): IJCSE October Edition

Section

Review Article

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