Word2vec’s Distributed Word Representation for Hindi Word Sense Disambiguation

Abstract

Word Sense Disambiguation (WSD) is the task of extracting an appropriate sense of an ambiguous word in a sentence. WSD is an essential task for language processing, as it is a pre-requisite for determining the closest interpretations of various language-based applications. In this paper, we have made an attempt to exploit the word embedding for finding the solution for WSD for the Hindi texts. This task involves two steps - the creation of word embedding and leveraging cosine similarity to identify an appropriate sense of the word. In this process, we have considered two mostly used word2vec architectures known as Skip-Gram and Continuous Bag-Of-Words [2] models to develop the word embedding. Further, we have chosen the sense with the closest proximity to identify the meaning of an ambiguous word. To prove the effectiveness of the proposed model, we have performed experiments on large corpora and have achieved an accuracy of nearly 52%.