Classifying Temporal Relations with Rich Linguistic Knowledge

Jennifer D'Souza and Vincent Ng.
Main Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 918--927, 2013.

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Abstract

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 demonstrate that our knowledge-rich, hybrid approach yields a 15--16% relative reduction in error over a state-of-the-art learning-based baseline system.

Ruleset

The hand-written rules used in the hybrid approach described in this paper are available from this page.

BibTeX entry

@InProceedings{DSouza+Ng:13a,
  author = {Jennifer D'Souza and Vincent Ng},
  title = {Classifying Temporal Relations with Rich Linguistic Knowledge},
  booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages = {918--927},
  year = 2013}