Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Finding legally relevant passages in case opinions
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
Toward adding knowledge to learning algorithms for indexing legal cases
ICAIL '99 Proceedings of the 7th 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
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Teaching case-based argumentation through a model and examples
Teaching case-based argumentation through a model and examples
Artificial Intelligence - Special issue on AI and law
Predicting outcomes of case based legal arguments
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Automatic summarisation of legal documents
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Automatic detection of arguments in legal texts
Proceedings of the 11th international conference on Artificial intelligence and law
Argument Schemes for Legal Case-based Reasoning
Proceedings of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference
Progress in textual case-based reasoning: predicting the outcome of legal cases from text
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
The Knowledge Engineering Review
Approaches to text mining arguments from legal cases
Semantic Processing of Legal Texts
Ontology framework for judgment modelling
AICOL'11 Proceedings of the 25th IVR Congress conference on AI Approaches to the Complexity of Legal Systems: models and ethical challenges for legal systems, legal language and legal ontologies, argumentation and software agents
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In this paper, we present methods for automatically finding abstract, legally relevant concepts in case texts and demonstrate how they can be used to make predictions of case outcomes, given the texts as inputs.In a set of experiments to test these methods, we focus on the open question of how best to represent legal text for finding abstract concepts. We compare different ways of representing legal case texts in order to test whether adding domain knowledge and some linguistic information can improve performance.We found that replacing individual names by roles in the case texts led to better indexing, and that adding certain syntactic and semantic information, in the form of Propositional Patterns that capture a sense of "who did what", led to better prediction. Our experiments also showed that of three learning algorithms, Nearest Neighbor worked best in learning how to identify indexing concepts in texts.In these experiments, we introduced a prototype system that can reason with text cases; it analyzes a case, predicts its outcome considering other cases in the database, and explains the prediction, all starting with a textual description of the case's facts as input.