Deep Learning-based Word Sense Disambiguation for Hindi Language Using Hindi WordNet Dataset

Pp: 140-159 (20)

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Abstract

This book chapter outlines an innovative approach to word sense disambiguation (WSD) for Hindi languages using deep learning. In natural language processing (NLP), WSD—which seeks to determine the precise meaning of the words within a specific context—is 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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