The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing
It addresses modality detection for NLP tasks like hedging and uncertainty, but is incremental as it builds on existing theoretical foundations.
This work tackles the problem of modality detection in NLP by proposing an event-based task that uses a comprehensive taxonomy to harmonize modal concepts, showing that detecting and classifying modal expressions is feasible and improves modal event detection.
Modality is the linguistic ability to describe events with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, but also improves the detection of modal events in their own right.