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 oversampling 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.
REF use temporal reasoning as an oversampling method to increase the amount of training data.
Machine Learning Of Temporal Relations
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics
Computer Science