Abstract
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.
Abstract (translated)
新兴班级培训数据不足甚至不可用，是包括文本分类在内的许多分类任务的一大挑战。因此，识别在学习阶段从未见过的类的文本文档，即所谓的零镜头文本分类，是很困难的，并且只有有限的以前的工作解决了这个问题。本文提出了一个结合数据增强和特征增强的两阶段框架来解决这一问题。将四种语义知识（嵌入词、类描述、类层次结构和一般知识图）整合到该框架中，有效地处理未知类的实例。实验结果表明，在零镜头场景下，每一个阶段和两个阶段的组合都达到了与基线和最近的分类方法相比的最佳总体精度。
URL
https://arxiv.org/abs/1903.12626
