Modelling legal argument: reasoning with cases and hypotheticals
Modelling legal argument: reasoning with cases and hypotheticals
Representation and learning in information retrieval
Representation and learning in information retrieval
C4.5: programs for machine learning
C4.5: programs for machine learning
A symbolic and connectionist approach to legal information retrieval
A symbolic and connectionist approach to legal information retrieval
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Natural language processing for information retrieval
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Proceedings of the 8th international conference on Artificial intelligence and law
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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
Pragmatic Legal Expert System
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
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Handbook of Natural Language Processing
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On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Teaching case-based argumentation through a model and examples
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Artificial Intelligence - Special issue on AI and law
Information extraction from case law and retrieval of prior cases
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Predicting outcomes of case based legal arguments
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Automatic summarisation of legal documents
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ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Information Extraction: Algorithms and Prospects in a Retrieval Context (The Information Retrieval Series)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An empirical investigation of reasoning with legal cases through theory construction and application
Artificial Intelligence and Law
Deep semantic interpretations of legal texts
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Automatic detection of arguments in legal texts
Proceedings of the 11th international conference on Artificial intelligence and law
The Carneades model of argument and burden of proof
Artificial Intelligence
Extractive summarisation of legal texts
Artificial Intelligence and Law - AI & law in eGovernment and eDemocracy part I
Argument Based Moderation of Benefit Assessment
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Disclosing false identity through hybrid link analysis
Artificial Intelligence and Law
Lexical Semantics and Expert Legal Knowledge towards the Identification of Legal Case Factors
Proceedings of the 2010 conference on Legal Knowledge and Information Systems: JURIX 2010: The Twenty-Third Annual Conference
Artificial Intelligence and Law
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Towards automatic generation of catchphrases for legal case reports
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Knowledge acquisition for categorization of legal case reports
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
Legal documents categorization by compression
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a set of classification concepts that capture stereotypical fact patterns that effect the strength of a legal claim, here trade secret misappropriation. Using these classifications, the program can evaluate and explain predictions about a problem's outcome given a database of previously classified cases. This paper provides an extended example illustrating both functions, prediction by IBP and text classification by SMILE, and reports empirical evaluations of each. While IBP's results are quite strong, and SMILE's much weaker, SMILE + IBP still has some success predicting and explaining the outcomes of case scenarios input as texts. It marks the first time to our knowledge that a program can reason automatically about legal case texts.