Paper: Predicting Unknown Time Arguments based on Cross-Event Propagation

ACL ID P09-2093
Title Predicting Unknown Time Arguments based on Cross-Event Propagation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors
  • Prashant Gupta (Indian Institute of Information Technology, Allahabad India)
  • Heng Ji (City University of New York-Graduate Center, New York NY)

Many events in news articles don’t include time arguments. This paper describes two methods, one based on rules and the other based on statistical learning, to predict the un- known time argument for an event by the propagation from its related events. The re- sults are promising – the rule based approach was able to correctly predict 74% of the un- known event time arguments with 70% preci- sion.