@inproceedings{cui-etal-2018-neural,
title = "Neural Open Information Extraction",
author = "Cui, Lei and
Wei, Furu and
Zhou, Ming",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2065",
doi = "10.18653/v1/P18-2065",
pages = "407--413",
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.",
}
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%0 Conference Proceedings
%T Neural Open Information Extraction
%A Cui, Lei
%A Wei, Furu
%A Zhou, Ming
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F cui-etal-2018-neural
%X 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.
%R 10.18653/v1/P18-2065
%U https://aclanthology.org/P18-2065
%U https://doi.org/10.18653/v1/P18-2065
%P 407-413
Markdown (Informal)
[Neural Open Information Extraction](https://aclanthology.org/P18-2065) (Cui et al., ACL 2018)
ACL
- Lei Cui, Furu Wei, and Ming Zhou. 2018. Neural Open Information Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 407–413, Melbourne, Australia. Association for Computational Linguistics.