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Deep Learning-based Word Sense Disambiguation for Hindi Language Using Hindi WordNet Dataset
- Authors: Preeti Yadav1, Sandeep Vishwakarma2, Sunil Kumar3
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View Affiliations Hide AffiliationsAffiliations: 1 M.J.P. Rohilkhand University, U.P., India 2 JCB UST YMCA Faridabad, Haryana, India 3 UIET, CSJM University, Kanpur, India
- Source: Federated learning for Internet of Vehicles: IoV Image Processing, Vision and Intelligent Systems , pp 140-159
- Publication Date: August 2024
- Language: English
Deep Learning-based Word Sense Disambiguation for Hindi Language Using Hindi WordNet Dataset, Page 1 of 1
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This book chapter outlines an innovative approach to word sense disambiguation (WSD) for Hindi languages using deep learning. In natural language processing (NLP), WSDwhich seeks to determine the precise meaning of the words within a specific contextis a crucial problem. The recommended approach learns and represents contextual word meanings using long short-term memory (LSTM) and convolutional neural networks (CNNs) capabilities of deep learning techniques. The huge Hindi WordNet dataset, which offers a wealth of semantic data on Hindi words, is used to train and assess the suggested method. Empirical findings show that the suggested methodology performs admirably on the Hindi WordNet dataset, outperforming a number of baseline techniques. This study showcases the latent deep learning techniques in addressing WSD challenges in the Hindi language, emphasizing the significance of leveraging semantic resources such as Hindi WordNet to enhance the efficacy of the NLP tasks in the domain of the Hindi language.
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