H5 TREC Legal Track Results.

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The Text REtrieval Conference (TREC) is an international research initiative sponsored by the National Institute of Standards and Technology (NIST), a division of the U.S. Department of Commerce. It was established in 1992 to support research for evaluation of text retrieval methodologies.

The stated goal of the TREC Legal Track is “to apply objective benchmark criteria for comparing search technologies” in the context of e-discovery, providing a concrete reference by which various technologies and vendors can be independently evaluated. H5’s results in the 2008 and 2009 TREC Studies are shown by the dots in the chart below. Further information on how to read and interpret this chart is provided below.


Source: See 2008 and 2009 TREC Studies at http://trec-legal.umiacs.umd.edu/. Note for 2008, results are shown for OCR-adjusted performance as discussed on page 36 of the 2008 TREC Overview paper.

Understanding this Chart

Precision and Recall are the two fundamental review system accuracy metrics that, when taken together, best convey the overall effectiveness, or accuracy, of a document review or search process. To understand the chart above, it’s important to first define Precision and Recall and why they matter.

Precision measures how many of the documents retrieved in a search are actually relevant, that is, how much of the result set is on target. For example, a 65 percent precision rate means that 65 percent of the documents retrieved are relevant, while 35 percent of those documents have been misidentified as relevant. Achieving high Precision means you produce only what you have to (maintain advantage) and keep costs down by reviewing only what you should (i.e. fewer non-relevant documents).

Recall measures how many of the relevant documents in a collection have actually been retrieved, that is, how much of the target set has been found. For example, a 40 percent recall rate means that 40 percent of all relevant documents in a collection have been found, and 60 percent have been missed. Achieving high Recall ensures you have what you need to produce (compliance) and, as important, what you need to win (you found as many relevant documents as possible).

Any document review process can achieve either high Recall or high Precision, but rarely both simultaneously. An effort to improve the performance of one factor generally causes the performance of the other to drop. This is often referred to as the “Recall-Precision tradeoff.” Put simply, it is fairly easy to achieve high Precision if you are willing to risk missing most of the relevant documents (low Recall). Conversely, when most search or review methods attempt to find most of the relevant documents (high Recall) they sacrifice Precision and return large numbers of false positives. Accordingly, the key is to know what level of Precision and Recall a search or review method achieved simultaneously.

The chart above plots Precision on the vertical axis and Recall on the horizontal axis, with points on the chart representing the simultaneous Precision and Recall achieved by a review system or process. The trade off effect between Precision and Recall described above is illustrated in the downward sloping red dotted line. The goal of any search or review process is to achieve results in the shaded gray box in the upper right corner: find nearly all of the relevant documents (high Recall) and have all that you found be relevant with very few false positives (high Precision).