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The Use of Text Mining Techniques in Electronic Discovery for Legal Matters
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Author(s): Michael W. Berry (University of Tennessee, USA), Reed Esau (Catalyst Repository Systems, USA)and Bruce Kiefer (Catalyst Repository Systems, USA)
Copyright: 2012
Pages: 17
Source title:
Next Generation Search Engines: Advanced Models for Information Retrieval
Source Author(s)/Editor(s): Christophe Jouis (Universite Paris III, France and LIP6-Universite Pierre et Marie Curie, France), Ismail Biskri (Universite du Quebec A Trois Rivieres, Canada), Jean-Gabriel Ganascia (LIP6 and CNRS-Universite Pierre et Marie Curie, France)and Magali Roux (LIP6 and CNRS-Universite Pierre et Marie Curie, France)
DOI: 10.4018/978-1-4666-0330-1.ch008
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Abstract
Electronic discovery (eDiscovery) is the process of collecting and analyzing electronic documents to determine their relevance to a legal matter. Office technology has advanced and eased the requirements necessary to create a document. As such, the volume of data has outgrown the manual processes previously used to make relevance judgments. Methods of text mining and information retrieval have been put to use in eDiscovery to help tame the volume of data; however, the results have been uneven. This chapter looks at the historical bias of the collection process. The authors examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization deal with nuances of the collection process.
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