Arguments Extracted from Text in Argument Based Machine Learning: A Case Study

We introduce a novel approach to cross-media learning based on argument based machine learning (ABML). ABML is a recent method that combines argumentation and machine learning from examples, and its main idea is to provide expert’s arguments for some of the learning examples. In this paper, we present an alternative approach, where arguments used in ABML are automatically extracted from text with a technique for relation extraction. We demonstrate and evaluate the approach through a case study of learning to classify animals by using arguments extracted from Wikipedia.