Predicting Unknown Time Arguments based on Cross-Event Propagation

摘要

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 unknown time argument for an event by the propagation from its related events. The results are promising - the rule based approach was able to correctly predict 74% of the unknown event time arguments with 70% precision.