Classifying Temporal Relations with Rich Linguistic Knowledge

Jennifer DSouza and Vincent Ng

We examine the task of temporal relation classification. 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 linguistic knowledge derived from a variety of semantic and discourse relations, rather than focusing on morpho-syntactic knowledge; and (3) leverage a novel combination of rule-based and learning-based approaches, rather than relying solely on one or the other. Experiments with the TimeBank corpus show that our knowledge-rich, hybrid approach yields a 15--16% relative reduction in error over a state-of-the-art learning-based baseline system.

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