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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-1077.pdf