State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weakly supervised open-world text classification, where supervision is only needed for a few examples from a few known classes and the machine should handle both known and unknown classes in test time. General open-world classification has been studied mostly using image classification; however, existing methods typically assume the availability of sufficient known-class supervision and strong unknown-class prior knowledge (e.g., the number and/or data distribution). We propose a novel framework WOT-Class that lifts those strong assumptions. Specifically, it follows an iterative process of (a) clustering text to new classes, (b) mining and ranking indicative words for each class, and (c) merging redundant classes by using the overlapped indicative words as a bridge. Extensive experiments on 7 popular text classification datasets demonstrate that WOT-Class outperforms strong baselines consistently with a large margin, attaining 23.33% greater average absolute macro-F1 over existing approaches across all datasets. Such competent accuracy illuminates the practical potential of further reducing human effort for text classification.

翻译：当前最先进的弱监督文本分类方法虽显著减少了所需的人工监督，但仍要求监督覆盖所有感兴趣的类别。当人类在缺乏完整信息的情况下探索新的大规模语料库时，这一条件在实践中极难满足。本文研究了一个新颖且重要的问题——弱监督开放世界文本分类，其中监督仅需涵盖少量已知类别的少量样本，而机器需在测试阶段同时处理已知和未知类别。通用开放世界分类主要基于图像分类进行研究，但现有方法通常假设已知类别监督充足且未知类别先验知识充分（如类别数量和/或数据分布）。我们提出了新型框架WOT-Class，消除了这些强假设。具体而言，它遵循迭代过程：(a) 将文本聚类为新类别，(b) 挖掘并排序每个类别的指示性词汇，(c) 通过重叠指示性词汇作为桥梁合并冗余类别。在7个主流文本分类数据集上的大量实验表明，WOT-Class以显著优势一致优于强基线方法，在所有数据集上相较于现有方法的平均绝对宏F1值提升23.33%。该准确率充分揭示了进一步减少文本分类人工干预的实际潜力。