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首页> 外文会议>CCF international conference on natural language processing and Chinese computing >BiTCNN: A Bi-Channel Tree Convolution Based Neural Network Model for Relation Classification
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BiTCNN: A Bi-Channel Tree Convolution Based Neural Network Model for Relation Classification

机译：BiTCNN：用于关系分类的基于双通道树卷积的神经网络模型

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摘要

Relation classification is an important task in natural language processing (NLP) fields. State-of-the-art methods are mainly based on deep neural networks. This paper proposes a bi-channel tree convolution based neural network model, BiTCNN, which combines syntactic tree features and other lexical level features together in a deeper manner for relation classification. First, each input sentence is parsed into a syntactic tree. Then, this tree is decomposed into two sub-tree sequences with top-down decomposition strategy and bottom-up decomposition strategy. Each sub-tree represents a suitable semantic fragment in the input sentence and is converted into a real-valued vector. Then these vectors are fed into a bi-channel convolutional neural network model and the convolution operations re performed on them. Finally, the outputs of the bi-channel convolution operations are combined together and fed into a series of linear transformation operations to get the final relation classification result. Our method integrates syntactic tree features and convolutional neural network architecture together and elaborates their advantages fully. The proposed method is evaluated on the SemEval 2010 data set. Extensive experiments show that our method achieves better relation classification results compared with other state-of-the-art methods.
机译：关系分类是自然语言处理（NLP）领域中的一项重要任务。最先进的方法主要基于深度神经网络。本文提出了一种基于双通道树卷积的神经网络模型BiTCNN，该模型将句法树特征和其他词法层次特征以更深层次的方式结合在一起，用于关系分类。首先，将每个输入句子解析为语法树。然后，通过自上而下的分解策略和自下而上的分解策略，将该树分解为两个子树序列。每个子树在输入语句中代表一个合适的语义片段，并被转换为实值向量。然后将这些向量输入到双通道卷积神经网络模型中，并对它们进行卷积运算。最后，将双通道卷积运算的输出组合在一起，并馈入一系列线性变换运算，以获得最终的关系分类结果。我们的方法将语法树特征和卷积神经网络体系结构集成在一起，并充分发挥了它们的优势。在SemEval 2010数据集上评估了所提出的方法。大量实验表明，与其他最新方法相比，我们的方法获得了更好的关系分类结果。

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