  Distributed word embeddings have yielded state-of-the-art performance in many
NLP tasks, mainly due to their success in capturing useful semantic
information. These representations assign only a single vector to each word
whereas a large number of words are polysemous (i.e., have multiple meanings).
In this work, we approach this critical problem in lexical semantics, namely
that of representing various senses of polysemous words in vector spaces. We
propose a topic modeling based skip-gram approach for learning multi-prototype
word embeddings. We also introduce a method to prune the embeddings determined
by the probabilistic representation of the word in each topic. We use our
embeddings to show that they can capture the context and word similarity
strongly and outperform various state-of-the-art implementations.
