期刊
KNOWLEDGE-BASED SYSTEMS
卷 194, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.105488
关键词
Relation extraction; Semantic relation; Natural language processing; Convolutional neural networks
资金
- National Key Research and Development Program of China [2017YFB1402401]
- Fundamental Research Funds for the Central Universities [2018CDYJSY0055]
- Graduate Research and Innovation Foundation of Chongqing [CYB18058]
- Social Undertakings and Livelihood Security Science and Technology Innovation Funds of CQ CSTC [cstc2017shmsA0641]
- Key Research Program of Chongqing Science and Technology Bureau [cstc2018jszx-cyzdX0086, cstc2019jscx-fxyd0142, cstc2017zdcy-zdyf0150]
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. (C) 2020 Elsevier B.V. All rights reserved.
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