Paper: Classifying Temporal Relations with Rich Linguistic Knowledge

ACL ID N13-1112
Title Classifying Temporal Relations with Rich Linguistic Knowledge
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013

We examine the task of temporal relation clas- sification. Unlike existing approaches to this task, we (1) classify an event-event or event- time pair as one of the 14 temporal relations defined in the TimeBank corpus, rather than as one of the six relations collapsed from the original 14; (2) employ sophisticated linguis- tic knowledge derived from a variety of se- mantic and discourse relations, rather than fo- cusing on morpho-syntactic knowledge; and (3) leverage a novel combination of rule-based and learning-based approaches, rather than re- lying solely on one or the other. Experiments with the TimeBank corpus demonstrate that our knowledge-rich, hybrid approach yields a 15?16% relative reduction in error over a state-of-the-art learning-based baseline sys- tem.