| 英文标题 | Text classification model with graph network based on semantic dependency parsing |
| 作者英文名 | Fan Guofeng, Liu Jing, Yao Shaowen, Luan Guikai |
| 机构英文名 | College of Software,Yunnan University,Kunming 650500,China |
| 英文摘要 | Due to the problem of the existing text classification methods which left out the semantic dependency information between words and required a lot of training data, this paper proposed a graph network text classification model TextSGN based on semantic dependency parsing. The model first performed semantic dependency parsing on the text, then in the semantic dependency graph, it performed word embedding and one-hot encoding on the nodes(single words) and edges(dependencies). In a further step, this paper proposed a SGN block to mine the semantic dependencies rapidly. The SGN block defined the way of information transmission from the structure level, updated the nodes and the edges in the graph to mine the semantic dependencies quickly and make the network converge faster. The experimental results on a set of open datasets show that Text-SGN achieves 95.2% accuracy in short text classification, which is 3.6% higher than the sub-optimal classification. |
| 英文关键词 | semantic dependency parsing; word embedding; semantic graph network block; text classification |