Humor Mechanic

Humor mechanics research aims to understand and computationally model the creation and recognition of humor, focusing on diverse forms like puns, one-liners, and broader comedic styles across various media (text, video). Current research employs transformer-based models and other deep learning architectures, often incorporating linguistic features (syntactic, semantic, contextual) to improve humor detection and generation. This field is significant for advancing natural language processing, enabling more sophisticated human-computer interaction and potentially leading to applications in creative writing, content generation, and sentiment analysis.