Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation
This addresses the problem of word sense ambiguity for NLP practitioners, offering an incremental improvement in unsupervised methods.
The paper tackles word sense ambiguity in NLP by proposing a context-aware approach for unsupervised word sense disambiguation, which substantially enhances disambiguation accuracy and surpasses existing techniques on a benchmark dataset.
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.