  Word sense disambiguation (WSD) methods identify the most suitable meaning of
a word with respect to the usage of that word in a specific context. Neural
network-based WSD approaches rely on a sense-annotated corpus since they do not
utilize lexical resources. In this study, we utilize both context and related
gloss information of a target word to model the semantic relationship between
the word and the set of glosses. We propose SensPick, a type of stacked
bidirectional Long Short Term Memory (LSTM) network to perform the WSD task.
The experimental evaluation demonstrates that SensPick outperforms traditional
and state-of-the-art models on most of the benchmark datasets with a relative
improvement of 3.5% in F-1 score. While the improvement is not significant,
incorporating semantic relationships brings SensPick in the leading position
compared to others.
