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Modeling Concept and Context to Improve Performance in eDiscovery

One condition of eDiscovery making it unique from other, more routine forms of IR is that all documents retrieved are settled by human inspection. Automated IR tools are used to reduce the size of a corpus search space to produce smaller sets of documents to be reviewed. However, a limitation associated with automated tools is they mainly employ statistical use of search terms that can result in poor performance when measured by recall and precision. One reason for this limitation is that relevance -the quality of matching a document to user criteria is dynamic and fluid, whereas a query -representing the translation of a user’s IR goal – is fixed. This paper reports on a design approach to eDiscovey that combines concept and context modeling to enhance search term performance. We apply this approach to the TREC 2011 Legal Track Problem Set #401. Our goal is to improve performance in eDiscovery IR results.