Parts 4 & 5:  Combining Predictive Coding and Search Term Classification in 5 Easy Steps

By Mark G. Walker, VP Advisory Services and 

Robin Athlyn Thompson, VP Marketing | Business Development

By popular demand, we are releasing Steps 4 & 5 together.  In case you missed Part 1, you can find it here.  You can find part 2 here, and part 3 here. 

Introduction to Steps 4 & 5.

Steps 4 & 5 are frequently performed in parallel.  When available, predictive coding is beneficial in validating key terms. 

Step 4:  Validate Key Terms Before You Agree to Them

There are those of us who have spent decades developing key term validation protocols, keeping the attorneys involved on task, and hopefully convincing them not to agree to poor key terms.  Poor key terms can, and frequently do, return 70%, 80%, even more than 90% documents that have little or no value to the case.  Key terms are usually overly broad.  In the search-world we call this “over-fitting,” A certain amount of over-fitting is desirable, as you don’t want to be too narrow with key terms as something can be missed.  On the other hand, you don’t want to be too broad, because the more you must review, the greater the cost and the more likely it will be that the opposition will fuss about dumping.  Not that dumping ever happens in this business!  Just like Goldilocks and the three bears, we’re aiming for key terms that are just right. 

There are entire protocols and technology features dedicated to validating search terms.  Oversimplified, a search term validation process is one that is repeatable and contains quality control measures.  Documents hitting a proposed set of search terms are “sampled” and those samples are reviewed and scored.  

Key Term	Hits Sampled	Tagged Relevant	% Relevant
Diamond	100	20	20%
Joe	100	10	10%

Imagine a case about a fictional restaurant called Diamond Joe’s.  The restaurant chain is owned by the fictional company Diamond Joe Holding.  The majority shareholder is the fictional Joe Diamond.  Joe owns an interest in many companies, some completely unrelated to the subject of the litigation, the restaurant chain.  Joe owns a diamond mine in South Africa – Joe’s Diamond Mines.  Joe also owns a chain of jewelry stores in South Texas and Mexico. Finally, Joe owns a minor-league baseball team named, you got it – The Diamondbacks.  As you might imagine, searching Joe Diamond’s email collection along with 50 of his employees will yield a great number of “false positives” using the terms diamond and Joe.  Of course, that seems obvious in this example, but there are many terms that have multiple meanings and depend on context.   Sampling hits of those terms, along with any others you have, will eventually ferret out which terms can be changed by, dropping some terms like Joe and diamond, and/or adding other terms, proximity connectors and other tweaks to existing and new terms.  Search term validation protocols are very effective in doubling and even tripling the relevancy rate of documents that you ultimately must review.  The cost savings is dramatic because even without leveraging advanced technology outlined in Step 5, far fewer documents are reviewed and of those reviewed; far fewer are of no value. 

On large projects, search term validation protocols can be tedious, but are necessary.  Your protocol must be repeatable, reportable, and iterative with validation and verification.  While sound key term validation protocols get you to the same place, the road is much shorter when you measure key term effectiveness as you conduct your sampling using the advanced analytics and strong key term reporting as outlined in Step 5.

Step 5: Leverage Smart Technology

Before classifying ESI in an analytics engine, perform any additional objective filtering that you can to eliminate ESI that has no value in a text classification engine, or is known to be irrelevant.  As previously discussed, audio and video files, image only file formats can often be eliminated from classification.  Eliminate ESI that may have survived prior filters, and sometimes can more easily be identified once in the review platform where predictive coding is delivered and available.  Establish a separate work flow for files that can’t be classified. If your using the right technology and provider, this will be part of their standard process, but be certain.

Advanced analytics, such as predictive coding or machine learning, is not new.  The technology and methods that underlay analytical engines has been in use, well, since computers to run them have existed.  In eDiscovery and Information Governance software platforms, predictive coding technology has been available for well over a decade.  However, it is only recently that lawyers and judges have truly begun to become comfortable with Predictive Coding technology and associated workflows.  Predictive Coding is a large bucket of all types of analytics tools, all of which are useful for different reasons.  Here, however, we are focused solely on machine learning.  Machine learning (ML) is the sub-field of computer science that gives computers the ability to learn, without being explicitly programmed (Arthur Samuel, 1959). (Samuel, 2000)  ML evolved from the study of pattern recognition and the computational learning theory in artificial intelligence. (Encyclopedia Britannica, n.d.)  Sounds a bit like rocket science?  Well, at its core, technology built on machine learning is full of complex algorithms, equations, hyper-planes and all kinds of complex things that frankly none of us really need to understand.  To someone like me, it is rocket science.  What we do need to understand is this: ML allows you to review samples of documents, mark them relevant or not relevant, and the technology will classify everything based upon human review of those exemplars.  The technology finds everything that is like those documents that are marked as relevant or not relevant.  Like any evolving technology, however, you must make sure you have a basic understanding of the technology you intended to use.  

Many of the ML engines used for predictive coding today were not originally built for predictive coding.  They were in fact built on methodologies and algorithms intended for concept classification analytics and visualization (reporting) of concepts.  The clear majority of the predictive coding engines on the market today, are passive learning applications.  Passive learning applications classify ESI as a snapshot in time.  You then review representative conceptual samples from the target population that are randomly selected by the application you are using.  Once the sample is reviewed, the ML engine determines what it thinks is relevant or not relevant based on that snapshot.  Many samples are reviewed in this process, and sometimes many re-classifications must occur.  Because a passive engine is a static snapshot of the data, samples must be larger in number, and there are many starts and stops as you train the machine to determine what is relevant as opposed to what is not relevant.  Like search term validation protocols without ML, with passive ML you get to the same spot down the road as an active learning ML, it just takes you longer to get there.  One has to review dramatically more samples and you must have substantial assistance to conduct reclassification and to measure stability.”  Stability is that point where you know that the machine has learned all it is going to learn from samples, and it is time to stop training and conduct quality control audits.  Determining stabilization in a passive learning based tool can be challenging.

Active learning ML-based technology is different.  Active learning engines are usually based upon binary methods and algorithms such as Support Vector Machine (SVM), for example (Saha, Hasan, Burgess, Habib, & Johnson, 2015).  Active learning changed the game with respect to speed and efficiency.  The biggest advantage to the consumer, is that the engine continually and “actively” reclassifies what is relevant as the sample review is being conducted.  With the right active learning engine, this reclassification happens virtually in real time no matter the number of reviewers.  Feedback on how you are doing is also immediate and continuous.

  

So how does ML help with the all-important key term validation?  Simple: because the classification engine is classifying all documents in a targeted ESI population, allowing you to grade the effectiveness as you go, you have real-time feedback on search term effectiveness - assuming, of course, that the technology you are using has strong key term hit reporting.  With ML you are not limited to just the sample documents that you review.  The machine takes what has been reviewed, and then extrapolates that to the entire population of data.  Your search term hit report can then provide a relevancy hit rate across all data, not just what has been reviewed.  As learning stabilizes, so too do the key terms, allowing you to quickly determine which terms need work.  The technology will often suggest terms by showing you those terms that are most common in relevant documents.

Once learning has stabilized, follow a well-established audit sample review to make sure that you agree that the learning has stabilized.  It is then time to move on to privilege review and production.

Conclusion

Well-established filtering, key term validation and machine learning workflows are becoming common place and for very good reason – combining the two has proven over and over to save considerable time and money by eliminating ESI that has no value.  In our world, time is indeed money.  

References

Enclycopedia Britannica. (n.d.). Machine Learning. Retrieved from Britannica: http://www.britannica.com/EBchecked/topic/1116194/machine-learning

National Institutes of Standards and Technoloy. (n.d.). National Software Reference Library. Retrieved from National Software Reference Library: https://www.nist.gov/programs-projects/national-software-reference-library

Saha, T., Hasan, M., Burgess, C., Habib, M., & Johnson, J. (2015). Batch-mode active learning for technology-assisted review. Big Data (Big Data), 2015 IEEE International Conference on (pp. 1134-1143). Santa Clara, California: IEEE.

Samuel, A. (2000). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research & Development, 44(1/2), 207.