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2009 First Asian Conference on Intelligent Information and Database Systems
Argument Based Machine Learning from Examples and Text
Dong hoi, Quang binh, Vietnam
April 01-April 03
ISBN: 978-0-7695-3580-7
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 use arguments for some of the learning examples. Arguments are usually provided by a domain expert. 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 automatically extracted from Wikipedia.
Index Terms:
Machine Learning, Argumentation, Relation extraction, Cross-Media
Citation:
Martin Možina, Claudio Giuliano, Ivan Bratko, "Argument Based Machine Learning from Examples and Text," aciids, pp.18-23, 2009 First Asian Conference on Intelligent Information and Database Systems, 2009
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