  The issue of word sense ambiguity poses a significant challenge in natural
language processing due to the scarcity of annotated data to feed machine
learning models to face the challenge. Therefore, unsupervised word sense
disambiguation methods have been developed to overcome that challenge without
relying on annotated data. This research proposes a new context-aware approach
to unsupervised word sense disambiguation, which provides a flexible mechanism
for incorporating contextual information into the similarity measurement
process. We experiment with a popular benchmark dataset to evaluate the
proposed strategy and compare its performance with state-of-the-art
unsupervised word sense disambiguation techniques. The experimental results
indicate that our approach substantially enhances disambiguation accuracy and
surpasses the performance of several existing techniques. Our findings
underscore the significance of integrating contextual information in semantic
similarity measurements to manage word sense ambiguity in unsupervised
scenarios effectively.
