The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing
| Autor: | Paul Portner, Shoval Sadde, Reut Tsarfaty, Valentina Pyatkin, Aynat Rubinstein |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Class (computer programming) Modality (human–computer interaction) Computer Science - Computation and Language Event (computing) business.industry Computer science Event based computer.software_genre Task (project management) Modal restrict Taxonomy (general) ComputerApplications_MISCELLANEOUS Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | ACL/IJCNLP (1) |
| DOI: | 10.48550/arxiv.2106.08037 |
| Popis: | 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. Comment: ACL 2021 |
| Databáze: | OpenAIRE |
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