Learning Event Durations from Event Descriptions

Feng Pan, Rutu Mulkar-Mehta, Jerry R. Hobbs

Abstract: We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.