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.
['Inderjeet Mani', 'Marc Verhagen', 'Ben Wellner', 'Chong Min Lee', 'James Pustejovsky']
Machine Learning of Temporal Relations