Distributional Semantics

Distributional semantics studies word meaning by analyzing how words co-occur in text, aiming to represent semantic relationships as vectors or functions. Current research focuses on leveraging these representations within larger models, such as large language models and vision-language models, to improve tasks like synonym detection, few-shot learning, and even low-level robotic cognition. This approach offers a powerful way to capture nuanced semantic information and has significant implications for natural language processing, computer vision, and other fields requiring semantic understanding.