  In natural language processing, word-sense disambiguation (WSD) is an open
problem concerned with identifying the correct sense of words in a particular
context. To address this problem, we introduce a novel knowledge-based WSD
system. We suggest the adoption of two methods in our system. First, we suggest
a novel method to encode the word vector representation by considering the
graphical semantic relationships from the lexical knowledge-base. Second, we
propose a method for extracting the contextual words from the text for
analyzing an ambiguous word based on the similarity of word vector
representations. To validate the effectiveness of our WSD system, we conducted
experiments on the five benchmark English WSD corpora (Senseval-02,
Senseval-03, SemEval-07, SemEval-13, and SemEval-15). The obtained results
demonstrated that the suggested methods significantly enhanced the WSD
performance. Furthermore, our system outperformed the existing knowledge-based
WSD systems and showed a performance comparable to that of the state-of-the-art
supervised WSD systems.
