仅使用标签名称进行文本分类：一种语言模型自训练方法

当前的文本分类方法通常需要大量带有人标签的文档作为培训数据，这在实际应用中可能既昂贵又难以获得。人类可以执行分类而不会看到任何带标签的示例，而只能基于描述待分类类别的少量单词。..

Text Classification Using Label Names Only: A Language Model Self-Training Approach

Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified.In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90% accuracy on four benchmark datasets including topic and sentiment classification without using any labeled documents but learning from unlabeled data supervised by at most 3 words (1 in most cases) per class as the label name.