计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 230-236.doi: 10.11896/jsjkx.210600042
闫佳丹, 贾彩燕
YAN Jia-dan, JIA Cai-yan
摘要： 近年来,图神经网络在文本分类任务中得到了广泛应用。与图卷积网络相比,基于消息传递的文本级的图神经网络模型具有内存占用少和支持在线检测等优点。然而此类模型通常仅使用词共现信息为语料中的各个文本构建词汇图,导致获取到的信息缺少多样性。文中提出了一种基于双图神经网络信息融合的文本分类方法。该方法在保留原有词共现图的基础上,根据单词间的余弦相似度构建语义图,并通过阈值控制语义图的稀疏程度,更有效地利用了文本的多方位语义信息。此外,测试了直接融合和注意力机制融合两种方式对词汇图和语义图上学习到的文本表示融合的能力。实验使用R8和R52等12个文本分类领域常用的数据集来测试算法的精度,结果表明,与最新的TextLevelGNN,TextING和MPAD这3个文本级的图神经网络模型相比,双图模型能够有效提高文本分类的性能。
中图分类号:
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