An evaluation of retrieval effectiveness for a full-text document-retrieval system
Communications of the ACM
Building a test collection for complex document information processing
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 11th international conference on Artificial intelligence and law
Proceedings of the 17th ACM conference on Information and knowledge management
Human-aided computer cognition for e-discovery
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Document categorization in legal electronic discovery: computer classification vs. manual review
Journal of the American Society for Information Science and Technology
Artificial Intelligence and Law
Evaluation of information retrieval for E-discovery
Artificial Intelligence and Law
Network-based filtering for large email collections in E-discovery
Artificial Intelligence and Law
Artificial Intelligence and Law
Afterword: data, knowledge, and e-discovery
Artificial Intelligence and Law
Mathematical Specification and Logic Modelling in the context of IR
Proceedings of the 2013 Conference on the Theory of Information Retrieval
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In this work, we provide a broad overview of the distinct stages of E-Discovery. We portray them as an interconnected, often complex workflow process, while relating them to the general Electronic Discovery Reference Model (EDRM). We start with the definition of E-Discovery. We then describe the very positive role that NIST's Text REtrieval Conference (TREC) has added to the science of E-Discovery, in terms of the tasks involved and the evaluation of the legal discovery work performed. Given the critical nature that data analysis plays at various stages of the process, we present a pyramid model, which complements the EDRM model: for gathering and hosting; indexing; searching and navigating; and finally consolidating and summarizing E-Discovery findings. Next we discuss where the current areas of need and areas of growth appear to be, using one of the field's most authoritative surveys of providers and consumers of E-Discovery products and services. We subsequently address some areas of Artificial Intelligence, both Information Retrieval-related and not, which promise to make future contributions to the E-Discovery discipline. Some of these areas include data mining applied to e-mail and social networks, classification and machine learning, and the technologies that will enable next generation E-Discovery. The lesson we convey is that the more IR researchers and others understand the broader context of E-Discovery, including the stages that occur before and after primary search, the greater will be the prospects for broader solutions, creative optimizations and synergies yet to be tapped.