Abstract
Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences. In this paper, we propose a discourse-level global inference model to mine those temporal relations between event mentions in document-level, especially in nonadjacent sentences. Moreover, we provide various kinds of discourse-level constraints, which derived from event semantics, to further improve our global inference model. Evaluation on a Chinese corpus justifies the effectiveness of our discourse-level global inference model over two strong baselines.- Anthology ID:
- C16-1137
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1451–1460
- Language:
- URL:
- https://aclanthology.org/C16-1137
- DOI:
- Cite (ACL):
- Peifeng Li, Qiaoming Zhu, Guodong Zhou, and Hongling Wang. 2016. Global Inference to Chinese Temporal Relation Extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1451–1460, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Global Inference to Chinese Temporal Relation Extraction (Li et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1137.pdf