Sense Disambiguation

Word sense disambiguation (WSD) aims to resolve the ambiguity of words with multiple meanings based on their context. Current research focuses on leveraging deep learning models, particularly transformer architectures like BERT and its variants, often enhanced by semantic lexical resources like WordNet and incorporating techniques such as mixup and prompting strategies to improve accuracy, especially for less frequent senses. WSD is crucial for improving natural language understanding in various applications, including machine translation, question answering, and bias detection in high-stakes domains like healthcare, and advancements in the field directly impact the performance of numerous NLP tasks.