Text Classification Using a Graph Based on Relationships Between Documents-猫眼课题宝

DOI：

发表时间：

2022

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Hiromu Nakajima;Minoru Sasaki

文本分类根据单词的共现及其出现频率等线索确定文档的类型，迄今为止已通过各种方法进行了研究。使用图结构数据的传统文本分类方法以每个节点之间的边权重的形式表达单词之间以及单词与文档之间的关系。然后，将图输入图神经网络进行学习。然而，传统的方法并没有在图上表示文档之间的关系，因此不能直接考虑文档之间的关系。因此，我们提出了一种使用图考虑文档之间关系的文本分类方法。该方法通过将文档的相似度作为文档节点之间的边的权重添加到传统方法的图上，直接表达文档之间的关系。然后将构建的图输入图卷积神经网络进行学习。我们使用五个英语语料库（20NG、R52、R8、Ohsumed 和 MR）进行实验来评估所提出的方法。结果表明，与传统方法相比，所提出的方法提高了准确性，并且文档节点之间的关系的使用是有效的。实验结果还表明，该方法对于文档较长的数据集特别有效。

Text classification, which determines the genre of a document based on cues such as the co-occurrence of words and their frequency of occurrence, has been studied in various approaches to date. Conventional text classification methods using graph-structured data express relationships between words and between words and documents in the form of weights of edges between each node. Then, the graph is input to a graph neural network for learning. However, conventional methods do not represent the relationship between documents on the graph, and thus cannot directly consider the relationship between documents. Therefore, we propose a text classification method using the graph considers the relationships among documents. This method directly expresses the relationship between documents by adding the similarity of documents as weights of edges between document nodes to the graph of the conventional method. The constructed graph is then input to a graph convolutional neural network for learning. We conducted experiments using five English corpus (20NG, R52, R8, Ohsumed, and MR) to evaluate proposed method. The results show that the proposed method improves accuracy compared to the conventional method and that the use of relationships among document nodes is effective. Experimental results also show that the proposed method is particularly effective on datasets with relatively long documents.

Text Classification Using a Graph Based on Relationships Between Documents

文献摘要

相似文献