基于注意力门控图神经网络的文本分类

Text Classification Based on Attention Gated Graph Neural Network

针对现有的文本分类工作在生成文本表示时通常忽略单词之间语义交互的问题,提出了一种新的基于注意力门控图神经网络的文本分类模型,有效地利用单词的语义特征并在充分语义交互的基础上提高了文本分类的准确率。首先,将每个输入文本转换为独立图结构数据并提取单词节点的语义特征;其次,利用注意力门控图神经网络对单词节点的语义特征进行交互和更新;然后,使用基于注意力机制的文本池化模块提取语义特征具有判别性的单词节点,以构建文本图表示;最后,基于文本图表示实现有效的文本分类。实验结果表明,所提方法在文本数据集Ohsumed,R8,R52和MR上的准确率分别达到了70.83%,98.18%,94.72%与80.03%,优于现有的方法。

To address the problem that the existing text classification work usually ignores the semantic interaction between words when generating text representation,this paper proposes a novel text classification model based on attention gated graph neural network.It makes effective use of the semantic features of words and improves the accuracy of text classification based on the adequate semantic interaction.Firstly,each input text is converted to a single graph-structured data and the semantic features of word nodes are extracted.Secondly,attention gated graph neural network is used to interact and update the semantic features of word nodes.In addition,the attention-based text pooling module is used to extract the word nodes with discriminative semantic features to construct text graph representation.Finally,effective text classification is implemented based on the text graph representation.Experimental results show that the proposed method achieves an accuracy of 70.83%,98.18%,94.72%and 80.03%on Ohsumed,R8,R52 and MR datasets,respectively,and outperforms existing methods.