Argument based machine learning from examples and text
|Argument based machine learning from examples and text|
|Author(s)||Mozina M., Giuliano C., Bratko I.|
|Published in||Proceedings - 2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009|
|Keyword(s)||Unknown (Extra: Alternative approach, Cross-media, Domain experts, Machine-learning, Relation extraction, Wikipedia, Animals, Database systems, Robot learning, Education)|
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Argument based machine learning from examples and text is a 2009 conference paper written in English by Mozina M., Giuliano C., Bratko I. and published in Proceedings - 2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009.
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.
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