An evaluation of retrieval effectiveness for a full-text document-retrieval system
Communications of the ACM
Natural language vs. Boolean query evaluation: a comparison of retrieval performance
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Hierarchical Model for Clustering and Categorising Documents
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Productivity as a metric for visual analytics: reflections on e-discovery
Proceedings of the 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization
Some(what) grand challenges for information retrieval
ACM SIGIR Forum
Search User Interfaces
Document categorization in legal electronic discovery: computer classification vs. manual review
Journal of the American Society for Information Science and Technology
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Artificial Intelligence and Law
Afterword: data, knowledge, and e-discovery
Artificial Intelligence and Law
Detecting outlier sections in us congressional legislation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost, time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the intelligence of the reviewers and enabling collaborative work.