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
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 407–413
- Language:
- URL:
- https://aclanthology.org/P18-2065
- DOI:
- 10.18653/v1/P18-2065
- Cite (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.
- Cite (Informal):
- Neural Open Information Extraction (Cui et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/P18-2065.pdf