Paper: Machine Learning Of Temporal Relations

ACL ID P06-1095
Title Machine Learning Of Temporal Relations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006

This paper investigates a machine learn- ing 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 se- ries of increasingly sophisticated base- lines involving expansion of rules de- rived from human intuitions.