Supervised Word Sense Disambiguation with Recurrent Neural Network Model
Chandrakant D. Kokane1, Sachin D. Babar2
1Chandrakant D. Kokane, Reseach Scholar, Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, India.
2Dr. Sachin D. Babar, PhD Department of Computer Engineering, Sinhgad Institute of Technology, Lonavala, Pune, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1447-1453 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3391129219/2020©BEIESP | DOI: 10.35940/ijeat.B3391.129219
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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: Disambiguating words is a branch of artificial intelligence that deals with natural language processing. The dissatisfaction of the motive of the word deals with the polysemy of the ambiguous word, processing a single word in natural language, having two or more meanings where the corresponding context discriminates the meaning. Humans are intelligent enough to derive the meaning of the word because they are a biological neural network. Computers can be trained in such a way that they should function similarly to biological neural networks. There are four different suggested approaches to clutter as the knowledge-dependent approach and the machine learning based models which are further classified as supervised, semi-supervised and unpublished learning models. The purpose of this research is to improve better communication between computers and humans. The discussed model used a supervised learning approach with recurrent neural networks.
Keywords: Supervised learning, Recurrent neural network, Word sense disambiguation.