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Evaluation of information retrieval for E-discovery

Abstract

The effectiveness of information retrieval technology in electronic discovery (E-discovery) has become the subject of judicial rulings and practitioner controversy. The scale and nature of E-discovery tasks, however, has pushed traditional information retrieval evaluation approaches to their limits. This paper reviews the legal and operational context of E-discovery and the approaches to evaluating search technology that have evolved in the research community. It then describes a multi-year effort carried out as part of the Text Retrieval Conference to develop evaluation methods for responsive review tasks in E-discovery. This work has led to new approaches to measuring effectiveness in both batch and interactive frameworks, large data sets, and some surprising results for the recall and precision of Boolean and statistical information retrieval methods. The paper concludes by offering some thoughts about future research in both the legal and technical communities toward the goal of reliable, effective use of information retrieval in E-discovery.

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Notes

  1. See, e.g., Sarbanes-Oxley Act, Title 18 of the U.S. Code, Section 1519 (U.S. securities industry requirement to preserve email for 7 years); National Archives and Records Administration regulations, 36 Code of Federal Regulations Part 1236.22 (all email that is considered to fall within the definition of “federal records” under Title 44 of the U.S. Code, Section 3301, must be archived in either paper or electronic systems).

  2. See, e.g., Qualcomm v. Broadcom, 539 F. Supp. 2d 1214 (S.D. Cal 2007), rev’d 2010 WL 1336937 (S.D. Cal. Apr. 2, 2010); In re Fannie Mae Litigation, 552 F.3d 814 (D.C. Cir. 2009).

  3. http://www.forrester.com/Research/Document/Excerpt/0,7211,40619,00.html

  4. See also Report of Anton R. Valukas, Examiner, In re Lehman Brothers Holdings Inc. (U.S. Bankruptcy Ct. S.D.N.Y. March 11, 2010), vol. 7, Appx. 5 (350 billion pages subjected to dozens of Boolean searches), available at http://lehmanreport.jenner.com/

  5. See Practice Point 1 in (The Sedona Conference 2007b) (referred to herein as the “Sedona Search Commentary”).

  6. Zubulake v. UBS Warburg LLC, 217 F.R.D. 309, 311 (2003); see generally (The Sedona Conference 2007a).

  7. See Pension Committee of the University of Montreal Pension Plan et al. v Banc of America Securities LLC, et al., 2010 WL 184312, *1 (S.D.N.Y. Jan. 15, 2010) (“Courts cannot and do not expect that any party can reach a standard of perfection.”).

  8. See (The Sedona Conference 2007b) at 202.

  9. There is, at virtually all times, an admitted asymmetry of knowledge as between the requesting party (who does not own and therefore does not know what is in the target data collection), and the receiving or responding party (who does own the collection and thus in theory could know its contents). For an exploration into ethical questions encountered when the existence of documents is not reached by a given keyword search method, see (Baron 2009).

  10. For example, see, People of the State of California v. Philip Morris, et al., Case No. J.C.C.P. 4041 (Sup. Ct. Cal.) (December 9, 1998 consent decree incorporating terms of Master Settlement Agreement or “MSA”). These documents have for the most part been digitized using Optical Character Recognition (OCR) technology and are available online on various Web sites. See the Legacy Tobacco Documents Library, available at http://legacy.library.ucsf.edu/. Portions of the MSA collection have been used in the TREC Legal Track.

  11. As used by E-discovery practitioners, “keyword search” most often refers to the use of single query terms to identify the set of all documents containing that term as part of a pre-processing step to identify documents that merit manual review.

  12. (The Sedona Conference 2007b) at 201. See also (Paul and Baron 2007).

  13. Id. at 202–03; 217 (Appendix describing alternative search methods at greater length).

  14. Id. at 202–03.

  15. In re Lorazepam & Clorazepate Antitrust Litigation, 300 F. Supp. 2d 43 (D.D.C. 2004).

  16. J.C. Associates v. Fidelity & Guaranty Ins. Co., 2006 WL 1445173 (D.D.C. 2006).

  17. For example, see Medtronic Sofamor Danck, Inc. v. Michelson, 229 F.R.D. 550 (W.D. Tenn. 2003); Treppel v. Biovail, 233 F.R.D. 363, 368–69 (S.D.N.Y. 2006) (court describes plaintiff’s refusal to cooperate with defendant in the latter’s suggestion to enter into a stipulation defining the keyword search terms to be used as a “missed opportunity” and goes on to require that certain terms be used); see also Alexander v. FBI, 194 F.R.D. 316 (D.D.C. 2000) (court places limitations on the scope of plaintiffs’ proposed keywords in a case involving White House email).

  18. In addition to cases discussed infra, see, e.g., Dunkin Donuts Franchised Restaurants, Inc. v. Grand Central Donuts, Inc, 2009 WL 175038 (E.D.N.Y. June 19, 2009) (parties directed to meet and confer on developing a workable search protocol); ClearOne Communications, Inc. v. Chiang, 2008 WL 920336 (D. Utah April 1, 2008) (court adjudicates dispute over conjunctive versus disjunctive Boolean operators).

  19. 242 F.R.D. 139 (D.D.C. 2007)

  20. Id. at 148 (citing to (Paul and Baron 2007), supra).

  21. 537 F. Supp. 2d 14, 24 (D.D.C. 2008).

  22. Id. at 16 (quoting U.S. v. O’Keefe, 2007 WL 1239204, at *3 (D.D.C. April 27, 2007)) (internal quotations omitted).

  23. 537 F. Supp. 2d at 16.

  24. Based only on what is known from the opinion, it is admittedly somewhat difficult to parse the syntax used in this search string. One is left to surmise that the ambiguity present on the face of the search protocol may have contributed to the court finding the matter of adjudicating a proper search string to be too difficult a task.

  25. 537 F. Supp. 2d at 24.

  26. Equity Analytics v. Lundin, 248 F.R.D. 331 (D.D.C. 2008) (stating that in O’Keefe “I recently commented that lawyers express as facts what are actually highly debatable propositions as to efficacy of various methods used to search electronically stored information,” and requiring an expert to describe scope of proposed search); see also discussion of Victor Stanley, Inc. v. Creative Pipe, Inc., infra.

  27. 250 F.R.D. 251 (D. Md. 2008).

  28. Id. at 254.

  29. Id. at 256–57.

  30. Id. at 259 n.9.

  31. Id. at 260 n.10.

  32. William A. Gross Construction Assocs., Inc. v. Am. Mftrs. Mutual Ins. Co., 256 F.R.D. 134, 135 (S.D.N.Y. 2009).

  33. We note that at least one important decision has been rendered by a court in the United Kingdom, which in sophisticated fashion similarly has analyzed keyword choices by parties at some length. See Digicel (St. Lucia) Ltd. & Ors. v. Cable & Wireless & Ors., [2008] EWHC 2522 (Ch.).

  34. Strictly speaking, unlike for a set, it is not meaningful to refer to the “recall” or “precision” of a ranking of documents. The popular ranked-based measures of Recall@K and Precision@K (which measure recall and precision of the set of top-ranked K documents) nominally suggest a recall or precision orientation for ranking, but actually compare ranked retrieval systems identically on individual topics. One can observe the recall-precision tradeoff in a ranking, however, by varying the cutoff K; e.g., increasing K will tend to increase recall at the expense of precision.

  35. http://ag.ca.gov/tobacco/msa.php

  36. Clearwell Systems obtained the collection from Aspen Systems and performed the processing described in this section.

  37. A pilot Interactive task was run in 2007 (Tomlinson et al. 2008), but with a very different task design.

  38. As is common, we use “Boolean query” somewhat loosely to mean queries built using not just the three basic Boolean operators (and, or, not), but also truncation and (unordered) proximity operators.

  39. http://edrm.net/

  40. Software Engineering Institute, http://www.sei.cmu.edu/cmmi/general

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Acknowledgments

The authors first wish to thank a number of individuals who in discussions with the authors contributed ideas and suggestions that found their way into portions of the present paper, including Thomas Bookwalter, Gordon Cormack, Todd Elmer, Maura Grossman and Richard Mark Soley. Additionally, the TREC Legal Track would not have been possible without the support of Ellen Voorhees and Ian Soboroff of NIST; the faculty, staff and students of IIT, UCSF, Tobacco Documents Online, and Roswell Park Cancer Institute who helped build IIT CDIP or the LTDL on which it was based; Celia White (the 2006 Track expert interactive searcher); Venkat Rangan of Clearwell Systems who helped to build the TREC Enron test collection; Richard Braman of The Sedona Conference® and the hundreds of law students, lawyers and Sedona colleagues who have contributed pro bono time to the project. Finally, the authors wish to thank Kevin Ashley and Jack Conrad for their support of and participation in the First and Third DESI Workshops, held as part of the Eleventh and Twelfth International Conferences on Artificial Intelligence and Law, at which many of the ideas herein were discussed.

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Correspondence to Douglas W. Oard.

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The first three sections of this article draw upon material in the introductory sections of two papers presented at events associated with the 11th and 12th International Conferences on Artificial Intelligence and Law (ICAIL) (Baron and Thompson 2007; Zhao et al. 2009) as well as material first published in (Baron 2008), with permission.

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Oard, D.W., Baron, J.R., Hedin, B. et al. Evaluation of information retrieval for E-discovery. Artif Intell Law 18, 347–386 (2010). https://doi.org/10.1007/s10506-010-9093-9

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Keywords

  • E-discovery
  • Information retrieval
  • Interactive search
  • Evaluation