Weakly-supervised text classification aims to train a classifier using only
class descriptions and unlabeled data. Recent research shows that
keyword-driven methods can achieve state-of-the-art performance on various
tasks. However, these methods not only rely on carefully-crafted class
descriptions to obtain class-specific keywords but also require substantial
amount of unlabeled data and takes a long time to train. This paper proposes
FastClass, an efficient weakly-supervised classification approach. It uses
dense text representation to retrieve class-relevant documents from external
unlabeled corpus and selects an optimal subset to train a classifier. Compared
to keyword-driven methods, our approach is less reliant on initial class
descriptions as it no longer needs to expand each class description into a set
of class-specific keywords. Experiments on a wide range of classification tasks
show that the proposed approach frequently outperforms keyword-driven models in
terms of classification accuracy and often enjoys orders-of-magnitude faster
training speed.

本文提出了一种有效的弱监督分类方法 FastClass，它使用密集文本表示从外部无标签语料库中检索与类相关的文档，并选择最优子集来训练分类器，相比关键词驱动方法，我们的方法对初始类描述的依赖性更低且训练速度更快，实验证明在广泛的分类任务上，该方法的分类准确性通常优于关键词驱动模型并且训练速度对比关键词驱动方法快得多。