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The Word Sense Disambiguation system is widely used in many fields, including business, research, education, and government organizations. The availability of natural language data on the internet has grown in tandem with the rapid advancement of technology and the widespread use of the internet. Disambiguation is the best tool for avoiding the problem of misperception. The most important feature of this approach is that words with multiple meanings of nouns can be easily disambiguated according to the sense of the sentences. As a result of incorporating all of these factors, This Work will concentrate on the Accuracy of adjectives and verbs. Many natural languages, including English, German, Arabic, Assamese, and others, propose different methodologies; however, work on Word Sense Disambiguation in Hindi is limited. For Hindi Word Sense Disambiguation, the proposed algorithm employs a genetic algorithm. The dynamic configuration window function is used, including the ambiguous terms left and right. The central tenet of this method is that the target term and its surroundings share a common topic. With approximately 65.17&#x0025;, 72&#x0025;, and 74.1 &#x0025;, respectively, the proposed work has compared to existing approaches such as graph-based approaches, association rules approaches, and probabilistic latent semantic analysis approaches. The proposed method has an accuracy of 80&#x0025;, which indicates that it improves the existing work by 8&#x0025; in terms of Accuracy and other parameters. Results show that the proposed work generates more accurate results for Hindi word sense disambiguation.

Role of Genetic Algorithm in Optimization of Hindi Word Sense Disambiguation