Hybrid Framework of Convolution and Recurrent Neural Networks for Text Classification

Shengfei Lyu, Jiaqi Liu
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引用次数: 7

Abstract

Convolutional neural network (CNN) and recurrent neural network (RNN) are two popular architectures used in text classification. Traditional methods to combine the strengths of the two networks rely on streamlining them or concatenating features extracted from them. In this paper, we propose a novel method to keep the strengths of the two networks to a great extent. In the proposed model, a convolutional neural network is applied to learn a 2D weight matrix where each row reflects the importance of each word from different aspects. Meanwhile, we use a bidirectional RNN to process each word and employ a neural tensor layer that fuses forward and backward hidden states to get word representations. In the end, the weight matrix and word representations are combined to obtain the representation in a 2D matrix form for the text. We carry out experiments on a number of datasets for text classification. The experimental results confirm the effectiveness of the proposed method.
基于卷积和递归神经网络的文本分类混合框架
卷积神经网络(CNN)和递归神经网络(RNN)是两种常用的文本分类方法。结合两种网络优势的传统方法依赖于简化它们或将从中提取的特征连接起来。在本文中,我们提出了一种新的方法,可以在很大程度上保持两个网络的优势。该模型采用卷积神经网络学习二维权重矩阵,每一行从不同角度反映每个单词的重要性。同时,我们使用双向RNN来处理每个单词,并使用神经张量层融合前向和后向隐藏状态来获得单词表示。最后,将权重矩阵和单词表示相结合,得到文本的二维矩阵表示。我们在许多数据集上进行了文本分类实验。实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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