Hedge Detection

Hedge detection focuses on identifying linguistic expressions that soften or qualify statements, conveying uncertainty or minimizing commitment. Current research emphasizes developing machine learning models, particularly leveraging transformer architectures like BERT and GPT, to automatically detect hedges in various text types, including spontaneous narratives and formal documents. This work has implications for improving natural language understanding in diverse fields, from enhancing human-computer interaction to refining risk assessment in domains like finance and medicine, by enabling more nuanced interpretation of textual information. Furthermore, research explores the cognitive underpinnings of hedge usage and its role in human communication and learning.