Multilingual Wordnet sense Ranking using nearest context

In this paper, we combine methods to estimate sense rankings from raw text with recent work on word embeddings to provide sense ranking estimates for the entries in the Open Multilingual Wordnet (OMW).The existing Word2Vec Polyglot2 pre-trained models are only built for single word entries, we, therefore, re-train them with multiword expressions from the wordnets, so that multiword expressions can also be ranked. Thus this trained model gives embeddings for both single words and multiwords. The resulting lexicon gives a strong WSD in five languages.The results are evaluated for Semcor sense corpora for 5 languages using Word2Vec and Glove model. The Glove model achieves the average accuracy of 0.47 and Word2Vec achieves 0.31 for languages such as English, Italian, Indonesian, Chinese and Japanese. The experimentation on OMW sense ranking proves that the rank correlation mostly similar to the human ranking.Hence distributional semantics certainly aid in Wordnet Sense Ranking.