Word Sens

Word sense disambiguation (WSD) focuses on resolving the multiple meanings (senses) a word can have within a given context. Current research employs various approaches, including contextualized language models, hierarchical clustering techniques, and density matrix methods to model both polysemy and synonymy, often leveraging word embeddings and knowledge bases to improve accuracy. Advances in WSD are crucial for improving numerous natural language processing applications, such as machine translation and information retrieval, and also offer insights into how humans learn and represent word meanings. Furthermore, research is exploring the diachronic evolution of word senses and the impact of factors like discipline-specific jargon on meaning.