Name
Abstract | ||
|---|---|---|
Weakly-supervised text classification has received much attention in recent years for it can alleviate the heavy burden of annotating massive data. Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts. However, existing methods treat keywords independently, thus ignore the correlation among them, which should be useful if properly exploited. In this paper, we propose a novel framework called ClassKG to explore keyword-keyword correlation on keyword graph by GNN. Our framework is an iterative process. In each iteration, we first construct a keyword graph, so the task of assigning pseudo labels is transformed to annotating keyword subgraphs. To improve the annotation quality, we introduce a self-supervised task to pretrain a subgraph annotator, and then finetune it. With the pseudo labels generated by the subgraph annotator, we then train a text classifier to classify the unlabeled texts. Finally, we re-extract keywords from the classified texts. Extensive experiments on both long-text and short-text datasets show that our method substantially outperforms the existing ones |
Year | Venue | DocType |
|---|---|---|
2021 | EMNLP | Conference |
Volume | Citations | PageRank |
2021.emnlp-main | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingming Zhang | 1 | 2726 | 154.39 |
| Jiandong Ding | 2 | 41 | 3.51 |
| Yi Xu | 3 | 0 | 0.34 |
| Yingyao Liu | 4 | 0 | 0.34 |
| Shuigeng Zhou | 5 | 2089 | 207.00 |