A connectionist and symbolic hybrid for improving legal research
International Journal of Man-Machine Studies - AI and legal reasoning. Part 2
Representation of legal text for conceptual retrieval
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
Finding factors: learning to classify case opinions under abstract fact categories
Proceedings of the 6th international conference on Artificial intelligence and law
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Toward adding knowledge to learning algorithms for indexing legal cases
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
Automatic categorization of case law
Proceedings of the 8th international conference on Artificial intelligence and law
Communications of the ACM - Ontology: different ways of representing the same concept
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Modern Information Retrieval
First steps in building a model for the retrieval of court decisions
International Journal of Human-Computer Studies
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Information Processing and Management: an International Journal
Using patterns of thematic progression for building a table of contents of a text
Natural Language Engineering
Knowledge element extraction for knowledge-based learning resources organization
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
| Hi-index | 0.00 |
Effective retrieval of court decisions is important. Automatically identifying legal concepts in the decision texts would be very helpful. In this paper we investigate how a statistics for hypothesis testing, i.e., the likelihood ratio, can help in this task. We describe how this statistic can be used for detecting important multi-term phrases in the case texts, how it can be used to find correlated terms, and how it is a means for feature or topic signature selection in automated case categorization. The technology has been tested upon more than 600 US cases.