  Semantic Similarity is an important application which finds its use in many
downstream NLP applications. Though the task is mathematically defined,
semantic similarity's essence is to capture the notions of similarity
impregnated in humans. Machines use some heuristics to calculate the similarity
between words, but these are typically corpus dependent or are useful for
specific domains. The difference between Semantic Similarity and Semantic
Relatedness motivates the development of new algorithms. For a human, the word
car and road are probably as related as car and bus. But this may not be the
case for computational methods. Ontological methods are good at encoding
Semantic Similarity and Vector Space models are better at encoding Semantic
Relatedness. There is a dearth of methods which leverage ontologies to create
better vector representations. The aim of this proposal is to explore in the
direction of a hybrid method which combines statistical/vector space methods
like Word2Vec and Ontological methods like WordNet to leverage the advantages
provided by both.
