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
Finding legally relevant passages in case opinions
Proceedings of the 6th international conference on Artificial intelligence and law
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Bootstrapping Case Base Development with Annotated Case Summaries
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
The Role of Information Extraction for Textual CBR
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Artificial Intelligence - Special issue on AI and law
From manual knowledge engineering to bootstrapping: Progress in information extraction and NLP
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Combining case-based and model-based reasoning for predicting the outcome of legal cases
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
SOPHIA-TCBR: A knowledge discovery framework for textual case-based reasoning
Knowledge-Based Systems
Knowledge Extraction and Summarization for an Application of Textual Case-Based Interpretation
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
iReMedI - Intelligent Retrieval from Medical Information
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Using domain knowledge for ontology-guided entity extraction from noisy, unstructured text data
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
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
Case Retrieval Reuse Net (CR2N): An Architecture for Reuse of Textual Solutions
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Automatically classifying case texts and predicting outcomes
Artificial Intelligence and Law
Integrated approach to detect inconspicuous contents
WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
Approaches to text mining arguments from legal cases
Semantic Processing of Legal Texts
Applying machine translation evaluation techniques to textual CBR
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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This paper presents methods that support automatically finding abstract indexing concepts in textual cases and demonstrates how these cases can be used in an interpretive CBR system to carry out case-based argumentation and prediction from text cases. We implemented and evaluated these methods in SMILE+IBP, which predicts the outcome of legal cases given a textual summary. Our approach uses classification-based methods for assigning indices. In our experiments, we compare different methods for representing text cases, and also consider multiple learning algorithms. The evaluation shows that a text representation that combines some background knowledge and NLP combined with a nearest neighbor algorithm leads to the best performance for our TCBR task.