Enhancing word sense disambiguation through contextual embedding and optimization techniques���

Abstract

This study presents a new strategy for determining the meaning of words based on their use in text. Traditional methods struggle because they do not fully grasp the context around ambiguous words. Our framework combines powerful models that understand context, like BERT and ELMO, to precisely identify a word’s sense based on its language environment. These representations depict word meanings in different situations more accurately. This helps the model to predict various meanings of an unclear word. Additionally, we apply techniques to refine the model’s performance. For instance, we customize the representations specifically for making sense of words. This increases the model’s awareness of subtle clues. Experiments show our approach notably outperforms basic methods in correctly determining word senses. The blending of contextual representations and refinement strategies not only boosts overall accuracy for defining ambiguous words but also demonstrates flexibility in handling difficult examples across diverse language contexts.