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        <title>Jointly Extracting Relations with Class Ties via Effective Deep Ranking</title>
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    <abstract>Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Jointly Extracting Relations with Class Ties via Effective Deep Ranking
%A Ye, Hai
%A Chao, Wenhan
%A Luo, Zhunchen
%A Li, Zhoujun
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ye-etal-2017-jointly
%X Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.
%R 10.18653/v1/P17-1166
%U https://aclanthology.org/P17-1166/
%U https://doi.org/10.18653/v1/P17-1166
%P 1810-1820

Markdown (Informal)

[Jointly Extracting Relations with Class Ties via Effective Deep Ranking](https://aclanthology.org/P17-1166/) (Ye et al., ACL 2017)

• Jointly Extracting Relations with Class Ties via Effective Deep Ranking (Ye et al., ACL 2017)

ACL

• Hai Ye, Wenhan Chao, Zhunchen Luo, and Zhoujun Li. 2017. Jointly Extracting Relations with Class Ties via Effective Deep Ranking. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1810–1820, Vancouver, Canada. Association for Computational Linguistics.