Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

Fei Cheng, Yusuke Miyao


Abstract
Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a “common root” assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.).
Anthology ID:
P17-2001
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/P17-2001
DOI:
10.18653/v1/P17-2001
Bibkey:
Cite (ACL):
Fei Cheng and Yusuke Miyao. 2017. Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1–6, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths (Cheng & Miyao, ACL 2017)
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PDF:
https://aclanthology.org/P17-2001.pdf
Video:
 https://aclanthology.org/P17-2001.mp4