@inproceedings{wu-etal-2019-open,
title = "Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data",
author = "Wu, Ruidong and
Yao, Yuan and
Han, Xu and
Xie, Ruobing and
Liu, Zhiyuan and
Lin, Fen and
Lin, Leyu and
Sun, Maosong",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1021",
doi = "10.18653/v1/D19-1021",
pages = "219--228",
abstract = "Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised paradigms, without taking advantage of existing relational facts in knowledge bases (KBs) and their high-quality labeled instances. To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. Experiment results on two real-world datasets show that our framework can achieve significant improvements as compared with other state-of-the-art methods. Our code is available at \url{https://github.com/thunlp/RSN}.",
}
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<abstract>Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised paradigms, without taking advantage of existing relational facts in knowledge bases (KBs) and their high-quality labeled instances. To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. Experiment results on two real-world datasets show that our framework can achieve significant improvements as compared with other state-of-the-art methods. Our code is available at https://github.com/thunlp/RSN.</abstract>
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%0 Conference Proceedings
%T Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data
%A Wu, Ruidong
%A Yao, Yuan
%A Han, Xu
%A Xie, Ruobing
%A Liu, Zhiyuan
%A Lin, Fen
%A Lin, Leyu
%A Sun, Maosong
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wu-etal-2019-open
%X Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised paradigms, without taking advantage of existing relational facts in knowledge bases (KBs) and their high-quality labeled instances. To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. Experiment results on two real-world datasets show that our framework can achieve significant improvements as compared with other state-of-the-art methods. Our code is available at https://github.com/thunlp/RSN.
%R 10.18653/v1/D19-1021
%U https://aclanthology.org/D19-1021
%U https://doi.org/10.18653/v1/D19-1021
%P 219-228
Markdown (Informal)
[Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data](https://aclanthology.org/D19-1021) (Wu et al., EMNLP-IJCNLP 2019)
ACL
- Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. 2019. Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 219–228, Hong Kong, China. Association for Computational Linguistics.