基于知识增强的图卷积神经网络的文本分类

王婷,朱小飞,唐顾

Knowledge-enhanced graph convolutional neural networks for text classification

Ting WANG,Xiao-fei ZHU,Gu TANG

表 2 KEGCN模型在4个数据集上的分类准确性对比

Tab.2 Comparison of classification accuracy of KEGCN model on four datasets

模型 Acc 20NG OHSUMED R52 R8 CNN-rand 0.7693±0.0061 0.4387±0.0100 0.8537±0.0047 0.9402±0.0057 CNN-non-static 0.8215±0.0052 0.5844±0.0106 0.8759±0.0048 0.9571±0.0052 LSTM(pretrain) 0.7543±0.0172 0.5110±0.0150 0.9048±0.0086 0.9609±0.0019 Bi-LSTM 0.7318±0.0185 0.4927±0.0107 0.9054±0.0091 0.9631±0.0033 fastText 0.7938±0.0030 0.5770±0.0049 0.9281±0.0009 0.9613±0.0021 SWEM 0.8516±0.0029 0.6312±0.0055 0.9294±0.0024 0.9532±0.0026 Text-GCN 0.8634±0.0009 0.6836±0.0056 0.9356±0.0018 0.9707±0.0051 HETE-GCN 0.8715±0.0015 0.6811±0.0070 0.9435±0.0025 0.9724±0.0010 KEGCN 0.8822±0.0045 0.6971±0.0059 0.9451±0.0018 0.9741±0.0025