Weakly supervised text classification (WSTC), also called zero-shot or dataless text classification, has attracted increasing attention due to its applicability in classifying a mass of texts within the dynamic and open Web environment, since it requires only a limited set of seed words (label names) for each category instead of labeled data. With the help of recently popular prompting Pre-trained Language Models (PLMs), many studies leveraged manually crafted and/or automatically identified verbalizers to estimate the likelihood of categories, but they failed to differentiate the effects of these category-indicative words, let alone capture their correlations and realize adaptive adjustments according to the unlabeled corpus. In this paper, in order to let the PLM effectively understand each category, we at first propose a novel form of rule-based knowledge using logical expressions to characterize the meanings of categories. Then, we develop a prompting PLM-based approach named RulePrompt for the WSTC task, consisting of a rule mining module and a rule-enhanced pseudo label generation module, plus a self-supervised fine-tuning module to make the PLM align with this task. Within this framework, the inaccurate pseudo labels assigned to texts and the imprecise logical rules associated with categories mutually enhance each other in an alternative manner. That establishes a self-iterative closed loop of knowledge (rule) acquisition and utilization, with seed words serving as the starting point. Extensive experiments validate the effectiveness and robustness of our approach, which markedly outperforms state-of-the-art weakly supervised methods. What is more, our approach yields interpretable category rules, proving its advantage in disambiguating easily-confused categories.

翻译：弱监督文本分类（WSTC），也称为零样本或无数据文本分类，因其能够在动态开放的网络环境中对大量文本进行分类而日益受到关注，因为该方法仅需每个类别有限的种子词（标签名称）而非标注数据。借助近期流行的提示预训练语言模型（PLM），许多研究利用人工构建和/或自动识别的谓词来估计类别的可能性，但这些方法未能区分这些类别指示词的效果，更遑论捕获其相关性并根据未标注语料库实现自适应调整。本文为使PLM有效理解每个类别，首先提出一种新颖的基于逻辑表达式的规则知识形式来刻画类别含义；随后，我们开发了一种基于提示PLM的方法RulePrompt用于WSTC任务，该方法包含规则挖掘模块、规则增强的伪标签生成模块，以及使PLM与该任务对齐的自监督微调模块。在该框架下，分配给文本的不准确伪标签与关联类别的不精确逻辑规则以交替方式相互增强，从而建立了一个以种子词为起点的知识（规则）获取与利用的自迭代闭环。大量实验验证了我们方法的有效性和鲁棒性，其在性能上显著优于最先进的弱监督方法。此外，我们的方法生成了可解释的类别规则，证明了其在消除易混淆类别歧义方面的优势。