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.

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

Giuliano, Claudio;
2008

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/4826
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