Abstract
Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.- Anthology ID:
- D17-1108
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1027–1037
- Language:
- URL:
- https://aclanthology.org/D17-1108
- DOI:
- 10.18653/v1/D17-1108
- Cite (ACL):
- Qiang Ning, Zhili Feng, and Dan Roth. 2017. A Structured Learning Approach to Temporal Relation Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1027–1037, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- A Structured Learning Approach to Temporal Relation Extraction (Ning et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1108.pdf
- Data
- TempEval-3