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The search problem posed by large heterogeneous data sets in litigation: possible future approaches to research
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International Conference on Artificial Intelligence and Law archive
Proceedings of the 11th international conference on Artificial intelligence and law table of contents
Stanford, California
SESSION: Legal information retrieval table of contents
Pages: 141 - 147  
Year of Publication: 2007
ISBN:978-1-59593-680-6
Authors
Jason R. Baron  National Archives and Records Administration, College Park, MD
Paul Thompson  Dartmouth College, Hanover, NH
Sponsor
: International Association for Artificial Intelligence and Law
Publisher
ACM  New York, NY, USA
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ABSTRACT

Lawyers and their large institutional clients increasingly face the enormous problem of how to efficiently and efficaciously conduct searches for relevant documents in large heterogeneous electronic data sets, for the purpose of responding to litigation demands. Past research indicates that lawyers greatly overestimate their true rate of recall in civil discovery. The unprecedented size, scale, and complexity of electronically stored data now potentially subject to routine capture in litigation, for purpose of preservation, access, and review, presents information retrieval researchers with a series of important challenges to overcome. This paper describes the current context of e-discovery and discusses the potential for IR and AI research to address the challenges of conducting e-discovery. The TREC Legal Track is presented as a forum for the evaluation of e-discovery research and one new evaluation measure, elusion, is described, which has potential for addressing problems of measuring recall.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jason R. Baron: colleagues
Paul Thompson: colleagues