论文标题
用于文本分类的语义分层图神经网络
A semantic hierarchical graph neural network for text classification
论文作者
论文摘要
文本分类任务的关键是语言表示和重要信息提取，并且有许多相关研究。近年来，在文本分类中对图形神经网络（GNN）的研究逐渐出现并显示出其优势，但现有模型主要集中于将单词直接输入，因为图形节点介绍了gnn模型，而忽略了样本中不同语义结构信息的不同级别。为了解决这个问题，我们提出了一个新的层次图神经网络（HIEGNN），该图分别从Word级，句子级别和文档级别提取相应的信息。与几种基线方法相比，几个基准数据集的实验结果取得更好或相似的结果，这表明我们的模型能够从样品中获得更多有用的信息。
The key to the text classification task is language representation and important information extraction, and there are many related studies. In recent years, the research on graph neural network (GNN) in text classification has gradually emerged and shown its advantages, but the existing models mainly focus on directly inputting words as graph nodes into the GNN models ignoring the different levels of semantic structure information in the samples. To address the issue, we propose a new hierarchical graph neural network (HieGNN) which extracts corresponding information from word-level, sentence-level and document-level respectively. Experimental results on several benchmark datasets achieve better or similar results compared to several baseline methods, which demonstrate that our model is able to obtain more useful information for classification from samples.
