Implementation of Automatic Mesh Rules Generation by Word Features Relationship (AMRG-WFR) for Word Sense Disambiguation for Marathi Language

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Swati Kale
Ujwalla Gawande

Abstract

One common feature of natural languages is a single word having the different meanings. Detecting the correct sense of the word in the given context is known as word sense disambiguation. In Natural language processing WSD is always a challenging and difficult task. The accuracy of many systems like information retrieval, machine translation text summarization depends upon the module used to identify the correct sense of the word. In this paper we proposed an automatic mesh rule generation technique by word feature relationship for the task of word sense disambiguation for Marathi language. Marathi is one the most popular and morphologically rich language in India. We conducted all the experiments on the benchmark WordNet dataset developed at Indian Institute of Technology Bombay (IIT Bombay). The proposed algorithm contains three sections.In the first section supervised learning technique is used to create training data set. Mesh rules of object were created in second section and in last section the correct sense of ambiguous word matched with the given context. Results show that the proposed method has superior performance as compared with already developed approaches.

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How to Cite
Kale, S., & Gawande, U. . (2021). Implementation of Automatic Mesh Rules Generation by Word Features Relationship (AMRG-WFR) for Word Sense Disambiguation for Marathi Language. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.456

References

  1. A, N. 2015. Supervised word sense disambiguation for urdu using bayesian classification. Center for Research in Urdu Language Processing, Lahore, Pakistan.
  2. Chandak, M. 2015. An hybrid approach to word sense disambiguation with and without learned knowledge. International Journal on Natural Language Computing (IJNLC)Vol. 4, No.2.
  3. Haroon, R. P. Malayalam word sense disambiguation. IEEE international conference on computational intelligence and computing research (pp. 1–4). IEEE.
  4. Heo, Y. 2020. Hybrid sense classification method for large-scale word sense disambiguation. IEEE Access, vol. 8, pp. 27247-27256.
  5. Ide, N. 1998. word sense disambiguation :the state fo the art.a comprehensive overview. Computational Linguistics Massachusetts Institute of Technology Press.
  6. K.P.sruthi. May. Unsupervised approach to word sense disambiguation in malayalam procedia technology volume 4 2016. Elsevier.
  7. Kumar, R., . K. R. Natural language engineering: The study of word sense disambiguation in punjabi. An International Journal of Engineering Sciences,1, 230–238. ISSN: 2229–6913.
  8. Kumar, P. 2015. Word sense disambiguation for punjabi language using overlap based approach. Advances in Intelligent Informatics. vol 320. Springer.
  9. Mittel, A. K. 2015. A performance of svm with modified lesk approach for word sense disambiguation in hindi language. International journal of Research in Engineering and Technology eISSN: 2319-1163 — pISSN: 2321-7308.
  10. Navigli, R. 2010. An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE transactions on pattern analysis and machine intelligence, vol. 32.
  11. pal Singh. Naive bayes classifier for word sense disambiguation of punjabi language 31(3).
  12. Shashank, N. S., . K. 2017. Word sense disambiguation of polysemy words in kannada language. International Conference on Advances in Computing,Communications and Informatics (ICACCI) (pp. 641–644). IEEE.
  13. Zungare. 2016. Wsd for marathi langauge words using decision graph tree. IEEE sponsored word conference on futurestic trends in research and innovations for social welfare.