@inproceedings{hu-etal-2020-selfore,
title = "{S}elf{ORE}: Self-supervised Relational Feature Learning for Open Relation Extraction",
author = "Hu, Xuming and
Wen, Lijie and
Xu, Yusong and
Zhang, Chenwei and
Yu, Philip",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.299",
doi = "10.18653/v1/2020.emnlp-main.299",
pages = "3673--3682",
abstract = "Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.",
}
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<abstract>Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.</abstract>
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%0 Conference Proceedings
%T SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
%A Hu, Xuming
%A Wen, Lijie
%A Xu, Yusong
%A Zhang, Chenwei
%A Yu, Philip
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F hu-etal-2020-selfore
%X Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.
%R 10.18653/v1/2020.emnlp-main.299
%U https://aclanthology.org/2020.emnlp-main.299
%U https://doi.org/10.18653/v1/2020.emnlp-main.299
%P 3673-3682
Markdown (Informal)
[SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction](https://aclanthology.org/2020.emnlp-main.299) (Hu et al., EMNLP 2020)
ACL