David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Artificial Intelligence and Law 13 (1):53-73 (2005)
In this paper we discuss the application of a new machine learning approach – Argument Based Machine Learning – to the legal domain. An experiment using a dataset which has also been used in previous experiments with other learning techniques is described, and comparison with previous experiments made. We also tested this method for its robustness to noise in learning data. Argumentation based machine learning is particularly suited to the legal domain as it makes use of the justifications of decisions which are available. Importantly, where a large number of decided cases are available, it provides a way of identifying which need to be considered. Using this technique, only decisions which will have an influence on the rules being learned are examined.
|Keywords||argumentation legal information systems legal knowledge discovery machine learning rule induction|
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Citations of this work BETA
Maya Wardeh, Trevor Bench-Capon & Frans Coenen (2009). Padua: A Protocol for Argumentation Dialogue Using Association Rules. [REVIEW] Artificial Intelligence and Law 17 (3):183-215.
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