With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data. In this paper, we explore an Inductive Logic Programming approach for one-shot text classification. In particular, we explore the framework of Meta-Interpretive Learning (MIL), along with using common-sense background knowledge extracted from ConceptNet. Results indicate that MIL can learn text classification rules from a small number of training examples. Moreover, the higher complexity of chosen examples, the higher accuracy of the outcome.

翻译：随着人工智能在个性化任务中展现出日益强大的潜力，开发数据高效且无需数百或数千训练数据的新型机器学习技术变得至关重要。本文探讨了一种基于归纳逻辑编程的一次性文本分类方法。具体而言，我们研究了元解释学习框架，并结合从ConceptNet中提取的常识背景知识。实验结果表明，元解释学习能够从少量训练样本中学习文本分类规则。此外，所选样本的复杂度越高，最终结果的准确率也越高。