An Approach to Bodo Word Sense Disambiguation (WSD) Using Word2Vec

Natural language processing (NLP) is one of the most popular proliferating research fields nowadays in artificial intelligence and machine learning. NLP deals with numerous applications such as Sentiment Analysis, Speech Recognition, Summarization of Text, Social Media Analytics. However, one of the significant challenges in developing NLP tools is word ambiguity, i.e., a word can have more than one meaning. The process of determining an ambiguous word's precise meaning in a given context is known as word sense disambiguation (WSD). In this research work, we propose a WSD framework for the Bodo language using Word2Vec. Cosine similarity is used to assess how similar the sentence is to the corpora to interpret the ambiguous word. To the best of our knowledge, this is the first work on Bodo WSD.