Gujarati Language Model: Word Sense Disambiguation using Supervised Technique
Tarjni Vyas1, Amit Ganatra2
1Tarjni Vyas, Department of Computer Science and Engineering CSE, Institute of Technology, Nirma University, Ahmedabad (Gujarat), India.
2Amit Ganatra, Dean, Department of Technology and Engineering, Devang Patel Institute of Advance Technology & Research DEPSTAR, Charotar University of Science and Technology University, Anand (Gujarat), India.
Manuscript received on 20 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3740-3744 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14820982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1482.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Word Sense Disambiguation (WSD) is a complex problem as it entirely depends on the language convolutions. Gujarati language is a multifaceted language which has so many variations. In this paper, the debate has advanced two methodologies for WSD: knowledge-based and deep learning approach. Accordingly, the Deep learning approach is found to perform even better one of its shortcoming is the essential of colossal data sources without which getting ready is near incomprehensible. On the other hand, uses data sources to pick the implications of words in a particular setting. Provided with that, deep learning approaches appear to be more suitable to manage word sense disambiguation; however, the process will always be challenging given the ambiguity of natural languages.
Keywords: Word Sense Disambiguation, Gujarati Language, Deep Learning, Natural Language Processing, Lesk Algorithm, Wordnet.
Scope of the Article: Natural Language Processing