Argument based machine learning from examples and text

From WikiPapers
Jump to: navigation, search

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.

[edit] 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 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.

[edit] References

This section requires expansion. Please, help!

Cited by

Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.