Building explanations from rules and structured cases
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Information filtering: the computation of similarities in large corpora of legal texts
ICAIL '95 Proceedings of the 5th international conference on Artificial intelligence and law
More than wyshful thinking: AustLII's legal inferencing via the World Wide Web
Proceedings of the 6th international conference on Artificial intelligence and law
Abstracting of legal cases: the SALOMON experience
Proceedings of the 6th international conference on Artificial intelligence and law
Evaluating a learning environment for case-based argumentation skills
Proceedings of the 6th international conference on Artificial intelligence and law
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine Learning
Using Machine Learning for Assigning Indices to Textual Cases
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
What You Saw Is What You Want: Using Cases to Seed Information Retrieval
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Teaching case-based argumentation through a model and examples
Teaching case-based argumentation through a model and examples
A cognitive approach to judicial opinion structure: applying domain expertise to component analysis
Proceedings of the 8th international conference on Artificial intelligence and law
Improving the representation of legal case texts with information extraction methods
Proceedings of the 8th international conference on Artificial intelligence and law
Automatic categorization of case law
Proceedings of the 8th international conference on Artificial intelligence and law
Automatic text representation, classification and labeling in European law
Proceedings of the 8th international conference on Artificial intelligence and law
First steps in building a model for the retrieval of court decisions
International Journal of Human-Computer Studies
Improving Hazard Classification through the Reuse of Descriptive Arguments
ICSR-7 Proceedings of the 7th International Conference on Software Reuse: Methods, Techniques, and Tools
An algorithm for the induction of defeasible logic theories from databases
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Concept extraction from legal cases: the use of a statistic of coincidence
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Induction of defeasible logic theories in the legal domain
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Classification and clustering for case-based criminal summary judgments
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Generating legal arguments and predictions from case texts
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Argument based machine learning applied to law
Artificial Intelligence and Law - Argumentation in artificial intelligence and law
Information Processing and Management: an International Journal
Classifying criminal charges in chinese for web-based legal services
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Legal documents categorization by compression
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Case-based reasoning systems have shown great promise for legal argumentation, but their development and wider availability are still slowed by the cost of manually representing cases. In this paper, we present our recent progress toward automatically indexing legal opinion texts for a CBR system. Our system SMILE uses a classification-based approach to find abstract fact situations in legal texts. To reduce the complexity inherent in legal texts, we take the individual sentences from a marked-up collection of case summaries as examples. We illustrate how integrating a legal thesaurus and linguistic information with a machine learning algorithm can help to overcome the difficulties created by legal language. The paper discusses results from a preliminary experiment with a decision tree learning algorithm. Experiments indicate that learning on the basis of sentences, rather than full documents, is effective. They also confirm that adding a legal thesaurus to the learning algorithm leads to improved performance for some, but not all, indexing concepts.