Global Inference to Chinese Temporal Relation Extraction

Peifeng Li, Qiaoming Zhu, Guodong Zhou, Hongling Wang


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
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1451–1460
Language:
URL:
https://www.aclweb.org/anthology/C16-1137
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1137.pdf