Abstract
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.
Abstract (translated)
最先进的弱监督文本分类方法，尽管减少了所需人类监督的数量，但仍然需要监督覆盖所有感兴趣的类别。在实践中，人类在没有完整图像的情况下探索新的大型文本集合时，这是非常困难的。在本文中，我们研究了一种新型的、但非常重要的弱监督开放世界文本分类问题，该问题需要仅从几个已知类别中监督几个示例，机器应该在测试时间同时处理已知和未知的类别。开放世界分类通常使用图像分类方法进行研究；然而，现有的方法通常假设有足够的已知类别监督和强大的未知类别先验知识(例如数量或数据分布)。我们提出了一种新的框架—— WOT-Class，该框架克服了这些强有力的假设。具体来说，它采用迭代过程，(a)将文本分类到新类别，(b)挖掘和排名每个类别的指示词，(c)通过使用重叠指示词作为桥梁将重复的类别合并。对7个流行的文本分类数据集进行广泛的实验表明， WOT-Class在显著性上优于强大的基准模型，并在所有数据集上平均绝对 macro-F1值方面表现出更高的平均相对F1值。这种出色的准确性揭示了文本分类中进一步减少人类工作量的实用潜力。
URL
https://arxiv.org/abs/2305.12401
