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ACL
2006

Machine Learning of Temporal Relations

11 years 1 months ago
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.
Inderjeet Mani, Marc Verhagen, Ben Wellner, Chong
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Inderjeet Mani, Marc Verhagen, Ben Wellner, Chong Min Lee, James Pustejovsky
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