A comprehensive exploration of semantic relation extraction via pre-trained CNNs
Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this task, and it is called the XM-CNN. The XM-CNN utilizes word embedding and position embedding information. It is designed...
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| Veröffentlicht in: | Knowledge-based systems Jg. 194; S. 105488 |
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| Sprache: | Englisch |
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Amsterdam
Elsevier B.V
22.04.2020
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this task, and it is called the XM-CNN. The XM-CNN utilizes word embedding and position embedding information. It is designed to reinforce the contextual output from the MT-DNNKD pre-trained model. Our model effectively utilized an entity-aware attention mechanisms to detected the features and also adopts and applies more relation-specific pooling attention mechanisms applied to it. The experimental results show that the XM-CNN achieves state-of-the-art results on the SemEval-2010 task 8, and a thorough evaluation of the method is conducted. |
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| AbstractList | Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this task, and it is called the XM-CNN. The XM-CNN utilizes word embedding and position embedding information. It is designed to reinforce the contextual output from the MT-DNNKD pre-trained model. Our model effectively utilized an entity-aware attention mechanisms to detected the features and also adopts and applies more relation-specific pooling attention mechanisms applied to it. The experimental results show that the XM-CNN achieves state-of-the-art results on the SemEval-2010 task 8, and a thorough evaluation of the method is conducted. |
| ArticleNumber | 105488 |
| Author | Li, Lili Wang, Weinan Zhong, Jiang Li, Qing Li, Qi |
| Author_xml | – sequence: 1 givenname: Qing surname: Li fullname: Li, Qing email: liqing@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 2 givenname: Lili surname: Li fullname: Li, Lili email: lilili@cqu.edu.cn organization: School of Civil Engineering, Chongqing University, Chongqing, China – sequence: 3 givenname: Weinan surname: Wang fullname: Wang, Weinan email: wangweinan@pku.edu.cn organization: School of Mathematical Sciences, Peking University, Beijing, China – sequence: 4 givenname: Qi surname: Li fullname: Li, Qi email: liqi0713@foxmail.com organization: Department of Computer Science and Engineering, Shaoxing University, Shaoxing, China – sequence: 5 givenname: Jiang surname: Zhong fullname: Zhong, Jiang email: zhongjiang@cqu.edu.cn organization: College of Computer Science, Chongqing University, Chongqing, China |
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| Cites_doi | 10.1007/s10489-018-1179-1 10.18653/v1/D13-1137 10.1016/j.ins.2018.01.051 10.1016/j.knosys.2016.03.008 10.3115/v1/P14-2012 10.3115/v1/D14-1162 10.1016/j.knosys.2019.02.033 10.1016/j.ins.2017.12.045 10.1016/j.ins.2019.02.065 10.1109/ICCV.2015.279 10.1016/j.knosys.2017.09.008 10.1016/j.knosys.2015.11.002 10.1016/j.knosys.2016.04.015 10.1109/CVPR.2016.10 10.18653/v1/P16-2034 10.18653/v1/D15-1206 |
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| Keywords | Relation extraction Semantic relation Natural language processing Convolutional neural networks |
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| SubjectTerms | Attention Computer architecture Convolutional neural networks Embedding Extraction Information retrieval Natural language processing Relation extraction Semantic relation Semantic relations Semantics |
| Title | A comprehensive exploration of semantic relation extraction via pre-trained CNNs |
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