@inproceedings{lei-etal-2018-cooperative,
title = "Cooperative Denoising for Distantly Supervised Relation Extraction",
author = "Lei, Kai and
Chen, Daoyuan and
Li, Yaliang and
Du, Nan and
Yang, Min and
Fan, Wei and
Shen, Ying",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1036",
pages = "426--436",
abstract = "Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.",
}
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<abstract>Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Cooperative Denoising for Distantly Supervised Relation Extraction
%A Lei, Kai
%A Chen, Daoyuan
%A Li, Yaliang
%A Du, Nan
%A Yang, Min
%A Fan, Wei
%A Shen, Ying
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F lei-etal-2018-cooperative
%X Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.
%U https://aclanthology.org/C18-1036
%P 426-436
Markdown (Informal)
[Cooperative Denoising for Distantly Supervised Relation Extraction](https://aclanthology.org/C18-1036) (Lei et al., COLING 2018)
ACL
- Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, and Ying Shen. 2018. Cooperative Denoising for Distantly Supervised Relation Extraction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 426–436, Santa Fe, New Mexico, USA. Association for Computational Linguistics.