Performance of word sense disambiguation (WSD) is one of the challenging tasks in the area of natural language processing (NLP). Generation of sense annotated corpus for multilingual word sense disambiguation is out of reach for most languages even if resources are available. In this paper we propose an unsupervised method using word and sense embedding or improving the performance of these systems using untagged. Corpora and create two bags namely ontological bag and wiki sense bag to generate the senses with highest similarity. Wiki sense bag provides external knowledge to the system required to boost the disambiguation accuracy. We explore Word2Vec model to generate the sense bag and observe significant performance gain for our dataset.

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