Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision

Jianhao Yan, Lin He, Ruqin Huang, Jian Li, Ying Liu


Abstract
Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores the temporal relations among those supervising instances. This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot. For this purpose, we construct a dataset called WIKI-TIME which additionally includes the valid period of a certain relation of two entities in the knowledge base. We propose a novel neural model to incorporate both the temporal information encoding and sequential reasoning. The experimental results show that, compared with the best of existing models, our model achieves better performance in both WIKI-TIME dataset and the well-studied NYT-10 dataset.
Anthology ID:
N19-1107
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1019–1030
Language:
URL:
https://aclanthology.org/N19-1107
DOI:
10.18653/v1/N19-1107
Bibkey:
Cite (ACL):
Jianhao Yan, Lin He, Ruqin Huang, Jian Li, and Ying Liu. 2019. Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1019–1030, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision (Yan et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1107.pdf
Video:
 https://vimeo.com/360608466
Code
 ElliottYan/DS_Temporal