Modified lesk algorithm for word sense disambiguation in Bengali

This article presents a novel approach towards solving the problem of Word Sense Disambiguation (WSD) for Bengali Text. The algorithm used in this work is a modification of Lesk Algorithm. In the original algorithm, the overlap between the "context bag" and the "sense bag" items from the lexical resource (WordNet) are calculated using word pair matching. In the current approach the overlap is calculated by adopting semantic similarity measure using the fastText subword embeddings. The approach can efficiently handle unknown wordforms and discover the latent semantics of words. Significant progress has been made in WSD for English and other European Languages. Indian languages like Bengali still pose a formidable challenge. The dataset used for the work is individual sentences from the Bengali Wikipedia which is a huge collection of Bengali text ( 96 K Webpages with 1700 K sentences), the Indo WordNet for Bengali language and Bengali Online Dictionary. The results of the experiments performed are promising. The target words which have semantically distinct synsets in the WordNet give a high F1 score. The F1 score achieved is 80% which is well over the baseline and shows significant improvement over the other knowledge-based approaches tried on low resource Indian languages.