Ambiguous Word

Word ambiguity, where a single word has multiple meanings depending on context, is a significant challenge in natural language processing. Current research focuses on improving the ability of large language models (LLMs), particularly transformer-based architectures, to disambiguate words by incorporating contextual information from surrounding text or even sensorimotor associations. This involves developing new datasets and evaluation metrics, as well as refining algorithms like clustering techniques and leveraging extra-sentential context to enhance accuracy in tasks such as machine translation and speech recognition. Addressing word ambiguity is crucial for improving the performance and reliability of numerous NLP applications, including information retrieval and human-computer interaction.