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.
Keywords: Deep learning, Hindi language, Hindi wordNet, Natural language processing, Word sense disambiguation.
