Unsupervised Word Sense Disambiguation

Unsupervised word sense disambiguation (WSD) aims to automatically determine the correct meaning of a word in context without relying on manually labeled data, a significant challenge in natural language processing. Recent research focuses on improving context-aware similarity measures and leveraging existing knowledge bases, such as synonym sets and sememes, to generate distinct word embeddings for different senses of polysemous words. These advancements enhance the accuracy of WSD, impacting downstream applications like machine translation and information retrieval by improving the understanding of word meaning within text.