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.
Peifeng Li, Qiaoming Zhu, Guodong Zhou, and Hongling Wang. 2016.Global Inference to Chinese Temporal Relation Extraction. InProceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1451–1460, Osaka, Japan. The COLING 2016 Organizing Committee.
@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
Peifeng Li, Qiaoming Zhu, Guodong Zhou, and Hongling Wang. 2016.Global Inference to Chinese Temporal Relation Extraction. InProceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1451–1460, Osaka, Japan. The COLING 2016 Organizing Committee.