Machine Learning of Temporal Relations

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.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Inderjeet Mani, Marc Verhagen, Ben Wellner, Chong Min Lee, James Pustejovsky
• Keywords: ACL 2006, ACL 2007, Baselines Involving Expansion, Human Annotated Data, Maximum Entropy Classifier