Journal of Natural Language Processing
Online ISSN : 2185-8314
Print ISSN : 1340-7619
ISSN-L : 1340-7619
Paper
Stacking Approach to Temporal Relation Classification with Temporal Inference
Natsuda LaokulratMakoto MiwaYoshimasa Tsuruoka
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2015 Volume 22 Issue 3 Pages 171-196

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Abstract

Traditional machine-learning-based approaches to temporal relation classification use only local features, i.e., those relating to a specific pair of temporal entities (events and temporal expressions), and thus fail to incorporate useful information that could be inferred from nearby entities. In this paper, we use timegraphs and stacked learning to perform temporal inference for classification in the temporal relation classification task. In our model, we predict a temporal relation by considering the consistency of possible relations between nearby entities. Performing 10-fold cross-validation on the Timebank corpus, we achieve an F1 score of 60.25% using a graph-based evaluation, which is 0.90 percentage points higher than that of the local approach, outperforming other proposed systems.

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© 2015 The Association for Natural Language Processing
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