Weakly-supervised Text Classification Based on Keyword Graph

• URL: http://arxiv.org/abs/2110.02591v1
• Date: Wed, 6 Oct 2021 08:58:02 GMT
• Title: Weakly-supervised Text Classification Based on Keyword Graph
• Authors: Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu and Shuigeng Zhou
• Abstract summary: 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. With the pseudo labels generated by the subgraph annotator, we then train a text classifier to classify the unlabeled texts.
• Score: 30.57722085686241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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