Humor Detection

Humor detection in computational linguistics aims to automatically identify humor in various modalities, including text, images, and audio-visual data. Current research focuses on developing multimodal models, often employing transformer architectures and incorporating linguistic features (syntactic, semantic, contextual) alongside visual and acoustic cues, to improve accuracy and address the subjective nature of humor. These advancements are significant for improving human-computer interaction, enabling more nuanced analysis of social media content, and furthering our understanding of the cognitive processes underlying humor. The field is also actively addressing challenges related to data scarcity, cross-cultural differences in humor, and the interplay between humor and offense.