  Several language applications often require word semantics as a core part of
their processing pipeline, either as precise meaning inference or semantic
similarity. Multi-sense embeddings (M-SE) can be exploited for this important
requirement. M-SE seeks to represent each word by their distinct senses in
order to resolve the conflation of meanings of words as used in different
contexts. Previous works usually approach this task by training a model on a
large corpus and often ignore the effect and usefulness of the semantic
relations offered by lexical resources. However, even with large training data,
coverage of all possible word senses is still an issue. In addition, a
considerable percentage of contextual semantic knowledge are never learned
because a huge amount of possible distributional semantic structures are never
explored. In this paper, we leverage the rich semantic structures in WordNet
using a graph-theoretic walk technique over word senses to enhance the quality
of multi-sense embeddings. This algorithm composes enriched texts from the
original texts. Furthermore, we derive new distributional semantic similarity
measures for M-SE from prior ones. We adapt these measures to word sense
disambiguation (WSD) aspect of our experiment. We report evaluation results on
11 benchmark datasets involving WSD and Word Similarity tasks and show that our
method for enhancing distributional semantic structures improves embeddings
quality on the baselines. Despite the small training data, it achieves
state-of-the-art performance on some of the datasets.
