Semantic Change

Semantic change, the evolution of word meanings over time and across contexts, is studied to understand language dynamics and improve natural language processing (NLP) applications. Current research focuses on developing computational methods, often employing contextualized word embeddings and graph-based clustering techniques within various model architectures (e.g., transformers, autoencoders), to detect and characterize semantic shifts, including changes in word sense breadth, sentiment, and usage relations. These advancements are crucial for enhancing the accuracy and robustness of NLP tasks like machine translation, information retrieval, and chatbot development, as well as providing insights into sociocultural changes reflected in language evolution.