  Distributed representations of words learned from text have proved to be
successful in various natural language processing tasks in recent times. While
some methods represent words as vectors computed from text using predictive
model (Word2vec) or dense count based model (GloVe), others attempt to
represent these in a distributional thesaurus network structure where the
neighborhood of a word is a set of words having adequate context overlap. Being
motivated by recent surge of research in network embedding techniques
(DeepWalk, LINE, node2vec etc.), we turn a distributional thesaurus network
into dense word vectors and investigate the usefulness of distributional
thesaurus embedding in improving overall word representation. This is the first
attempt where we show that combining the proposed word representation obtained
by distributional thesaurus embedding with the state-of-the-art word
representations helps in improving the performance by a significant margin when
evaluated against NLP tasks like word similarity and relatedness, synonym
detection, analogy detection. Additionally, we show that even without using any
handcrafted lexical resources we can come up with representations having
comparable performance in the word similarity and relatedness tasks compared to
the representations where a lexical resource has been used.
