  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.
