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• W2236688737 abstract "Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task~8, and achieve an F1-score of 86.1%, outperforming previous state-of-the-art recorded results." @default.
• W2236688737 created "2016-06-24" @default.
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• W2236688737 date "2016-01-14" @default.
• W2236688737 modified "2023-09-25" @default.
• W2236688737 title "Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation" @default.
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