Event Embeddings

Event embeddings represent events as numerical vectors, enabling machines to understand and reason about event relationships within various contexts, such as text, audio, or social networks. Current research focuses on improving these embeddings by incorporating richer contextual information (e.g., event arguments, temporal relationships, and inter-event dependencies) using techniques like graph neural networks and temporal point processes. This work is significant for advancing natural language processing, acoustic scene classification, and other applications requiring sophisticated event analysis, leading to improved information extraction, prediction, and classification accuracy.