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.