Predictive Coding: Networks and Trees

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Inside the BoxIn describing predictive coding systems, it’s important to distinguish document-based systems from corpus-based systems. Document-based systems make their predictions based on the similarity of each document to a single, previously-categorized document. Corpus-based systems are, in addition, able to use higher-order properties of groups of previously-categorized documents to make their predictions. Because of this advantage, corpus-based systems are less affected by errors in coding individual documents. Continue reading

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Minds Matter: H5, Rules-Based TAR, and Cooperation

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dipole_small copyThis article is about how H5‘s rules-based approach to technology-assisted review provides a great framework for illustrating cooperation in ediscovery. But first, some context.

By this time next year, Rule 1 of the Federal Rules of Civil Procedure will have been amended to codify the principles of proportionality and cooperation between opposing counsel. Continue reading

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Predictive Coding: For What, Not For Whom

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PvPSome say that predictive coding isn’t as useful to plaintiffs as it is to defendants. See, for example, this post on Linkedin.

In my view, what really matters is whether the litigant is producing or receiving the documents. Predictive coding is more useful to a producing party than to the receiving party. And, in a way, predictive coding is actually the opposite of post-production analysis. Continue reading

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