Combining Recurrent and Convolutional Neural Networks for Relation Classification

机译：结合递归和卷积神经网络进行关系分类

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This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.

机译：本文研究了用于关系分类任务的两种不同的神经体系结构：卷积神经网络和递归神经网络。对于这两个模型，我们演示了不同架构选择的影响。我们提出了一种用于关系分类的卷积神经网络的新上下文表示（扩展的中间上下文）。此外，我们提出了连接主义双向递归神经网络，并介绍了对其优化的排名损失。最后，我们证明了使用简单的投票方案将卷积神经网络和递归神经网络相结合足以提高结果的准确性。我们的神经模型在SemEval 2010关系分类任务中获得了最先进的结果。

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《Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies》|2016年|534-539|共6页
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Ngoc Thang Vu; Heike Adel; Pankaj Gupta; Hinrich Schuetze;
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Combining Recurrent and Convolutional Neural Networks for Relation Classification

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