Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

Qiang Ning, Hao Wu, Haoruo Peng, Dan Roth


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 available.
Anthology ID:
N18-1077
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
841–851
Language:
URL:
https://aclanthology.org/N18-1077
DOI:
10.18653/v1/N18-1077
Bibkey:
Cite (ACL):
Qiang Ning, Hao Wu, Haoruo Peng, and Dan Roth. 2018. Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 841–851, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (Ning et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1077.pdf
Note:
 N18-1077.Notes.pdf