Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
This work addresses temporal relation extraction for natural language processing, but it is incremental as it builds on existing systems with a new resource.
The paper tackled the problem of extracting temporal relations from natural language by developing a probabilistic knowledge base from New York Times articles over 20 years, and showed that this resource improves existing temporal extraction systems.
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 available.