Machine learning of temporal relations

  • Authors:

  • Inderjeet Mani;Marc Verhagen;Ben Wellner;Chong Min Lee;James Pustejovsky

  • Affiliations:

  • The MITRE Corporation, Bedford, MA and Georgetown University, Washington, DC;Brandeis University, Waltham, MA;The MITRE Corporation, Bedford, MA and Brandeis University, Waltham, MA;Georgetown University, Washington, DC;Brandeis University, Waltham, MA

  • Venue:
  • ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
  • Year:
  • 2006

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Abstract

This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an over-sampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.