David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
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.
Similar books and articles
Stefania Costantini & Gaetano Aurelio Lanzarone (1995). Explanation-Based Interpretation of Open-Textured Concepts in Logical Models of Legislation. Artificial Intelligence and Law 3 (3):191-208.
Kuo-Chin Chang, Tzung-Pei Hong & Shian-Shyong Tseng (1996). Machine Learning by Imitating Human Learning. Minds and Machines 6 (2):203-228.
F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
Jonathan Ginzburg & Shalom Lappin, Using Machine Learning for Non-Sentential Utterance Classiﬁcation.
Abraham Meidan & Boris Levin (2002). Choosing From Competing Theories in Computerised Learning. Minds and Machines 12 (1):119-129.
Jon Williamson (2004). A Dynamic Interaction Between Machine Learning and the Philosophy of Science. Minds and Machines 14 (4):539-549.
John Zeleznikow (2002). An Australian Perspective on Research and Development Required for the Construction of Applied Legal Decision Support Systems. Artificial Intelligence and Law 10 (4):237-260.
Added to index2009-01-28
Total downloads2 ( #366,597 of 1,102,033 )
Recent downloads (6 months)1 ( #306,606 of 1,102,033 )
How can I increase my downloads?