【摘要】 给出了一种多通道卷积神经网络（Convolutional Neural Network, CNN）方法实现中文文本端到端的关系抽取.每个通道用分层的网络结构,在传播过程中互不影响,使神经网络能学习到不同的表示.结合中文语言的难点,加入注意力机制（Attention Mechanism, Att）获取更多的语义特征,并通过分段平均池化融入句子的结构信息.经过最大池化层获得句子的最终表示后,计算关系得分,并用排序损失函数（Ranking-Loss Function, RL）代替交叉熵函数进行训练.实验结果表明,提出的MCNN_Att_RL （Multi CNN_Att_RL）模型能有效提高关系抽取的查准率、召回率和F₁值.更多还原

【Abstract】 This paper presents an end-to-end method for Chinese text relation extraction based on a multichannel CNN（convolutional neural network）. Each channel is stacked with a layered neural network; these channels do not interact during recurrent propagation, which enables a neural network to learn different representations. Considering the nuances of the Chinese language, we employed the attention mechanism to extract the semantic features of a sentence, and then integrate structural information using piecewise average pooling. After the maximum pooling layer, the final representation of the sentence is obtained and a relational score is calculated. Finally, the ranking-loss function is used to replace the cross-entropy function for training. The experimental results show that the MCNN_Att_RL（Multi CNN_Att_RL）model proposed in this paper can effectively improve the precision, recall, and F₁ value of entity relation extraction.更多还原