Improving temporal relation extraction with a globally acquired statistical resource

Abstract

Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource ? a probabilistic knowledge base acquired in the news domain ? by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987?2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-Aware tasks. The proposed system and resource are both publicly available1.