Neural Open Information Extraction

Lei Cui, Furu Wei, Ming Zhou


Abstract
Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.
Anthology ID:
P18-2065
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
407–413
URL:
https://www.aclweb.org/anthology/P18-2065
DOI:
10.18653/v1/P18-2065
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PDF:
https://www.aclweb.org/anthology/P18-2065.pdf
Video:
 https://vimeo.com/285804053