Modal Verb

Modal verbs, words like "can," "may," and "must," express a speaker's perspective on the likelihood or possibility of an event, posing challenges for computational linguistics due to their nuanced meanings and context-dependency. Current research focuses on developing robust methods for automatically identifying and classifying the various functions of modal verbs, employing techniques like contrastive learning and neural network architectures such as RoBERTa, often trained on large annotated datasets specifically designed for this purpose. Accurate interpretation of modal verbs is crucial for improving natural language processing tasks such as question answering and information extraction from diverse text types, including scientific literature, where their use significantly impacts meaning and interpretation.