Performance Analysis of Sense Embeddings in Multilingual WSD Framework

Abstract

Execution of Word Sense Disambiguation (WSD) is one of the difficult undertakings in the space of Natural language processing (NLP). Age of sense clarified corpus for multilingual WSD is far off for most languages regardless of whether assets are accessible. In this paper we propose a solo technique utilizing word and sense embeddings for working on the presentation of WSD frameworks utilizing untagged corpora and make two bags to be specific context bag and wiki sense bag to create the faculties with most noteworthy closeness. Wiki sense bag gives outer information to the framework needed to help the disambiguation exactness. We investigate Word2Vec model to produce the sense back and notice huge execution acquire for our dataset.