@inproceedings{li-etal-2016-global,
title = "Global Inference to {C}hinese Temporal Relation Extraction",
author = "Li, Peifeng and
Zhu, Qiaoming and
Zhou, Guodong and
Wang, Hongling",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1137/",
pages = "1451--1460",
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."
}
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%0 Conference Proceedings
%T Global Inference to Chinese Temporal Relation Extraction
%A Li, Peifeng
%A Zhu, Qiaoming
%A Zhou, Guodong
%A Wang, Hongling
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-global
%X 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.
%U https://aclanthology.org/C16-1137/
%P 1451-1460
Markdown (Informal)
[Global Inference to Chinese Temporal Relation Extraction](https://aclanthology.org/C16-1137/) (Li et al., COLING 2016)
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.