Word Sense Disambiguation in Hindi Language Using Machine Learning

Abstract

In the realm of Natural Language Processing (NLP), Word Sense Disambiguation (WSD) is a fundamental task that involves determining the precise meaning or quotsensequot of a word within a given context, distinguishing it from other possible meanings associated with the same word. Effectively, this task resembles a classification problem, where the objective is to assign the most appropriate label (sense) to each word instance. newlineThis thesis presents a comprehensive investigation into Word Sense Disambiguation (WSD) within the context of the Hindi language, leveraging state-of-the-art machine learning techniques. While considerable progress has been made in English WSD, languages like Hindi, with their rich morphology and contextual nuances, present unique challenges. newlineIn this study, explored and compared various machine learning approaches, including supervised, unsupervised, and neural network-based models, for the task of Hindi WSD. Further, evaluated the performance of these methods on a diverse and extensive Hindi corpus, emphasizing the importance of linguistic and contextual features specific to Hindi. newlineResults demonstrated the effectiveness of machine learning in disambiguating word senses in Hindi, providing valuable insights into the adaptation of WSD techniques for languages with complex linguistic characteristics. Further, it also investigated the impact of various factors such as the hyperparameters, the role of domain-specific knowledge, and the incorporation of word embeddings in improving Hindi WSD accuracy. newlineThis research contributed to the broader goal of enhancing the accuracy and applicability of natural language processing tools for Hindi and similar languages, opening avenues for improved machine understanding and communication in multilingual environments. Additionally, the findings of this study have practical implications for the development of more precise machine translation, information retrieval, and text summarization systems in Hindi, ultimately facilitating better human-computer interaction in