Fusion of Fast-text and Indo-Wordnet for Disambiguation of Word Sense in the Marathi Language

Abstract

This research employs the combination of the FastText model and Indo-WordNet to address the issue of word sense disambiguation (WSD) in Marathi literature. The initial iteration of the algorithm employed word pair matching as the technique to ascertain the presence of overlap between the items in the "context bag" and the "sense bag" derived from the lexical resource WordNet. The current methodology involves the computation of overlap by utilizing a semantic similarity metric that leverages fastText subword embeddings. This approach demonstrates proficiency in effectively managing unanticipated word formations, while simultaneously elucidating the inherent semantics of the terms. Significant progress has been achieved in the field of Word Sense Disambiguation (WSD) for both the English language and many European languages. There is a substantial challenge to be surmounted in relation to Marathi and other languages spoken in India. The Marathi text corpus, sourced from the government of India, comprises a vast assemblage of Marathi sentences. The dataset used in this study consisted of the Indo WordNet for the Marathi language and the Marathi Online Dictionary. The results of the conducted experiments demonstrate promising discoveries. The target words that possess semantically distinct synsets in WordNet are assigned a high F1 score. The achieved F1 score of 89% above the baseline and signifies substantial advancements in compared to previous knowledge-based methodologies employed for low resource Indian languages.