Conceptual Search verses Predictive Coding


In my last blog entry titledSuccessful Predictive Coding Adoption is Dependent on Effective Information Governance”, a question was posted which I thought deserved a wider sharing with the group; “What is the difference between predictive coding and conceptual search?” Being an individual not directly associated with either technology but with some interesting background, I believe I can attempt to explain the differences, at least as it pertains to discovery processes.

Conceptual search technologies allow a user to search on concepts…(pretty valuable insight, right?) instead of searching on a keyword such as “dog”. In the case of a keyword search on “dog”, the user would generate a results set of every document/file/record with the three letters D-O-G present in that specific sequence. The results could include returns on “dogs”, the 4- legged animals, references to “frankfurters”, references to movies (Dog Day Afternoon) etc. in no particular priority.

True conceptual search capability understands (based on search criteria) that the user was looking for information on the 4-legged animals so would return references to not just “dogs” but would also include references to “Golden Retrievers”, “Animal Shelters”, “Pet Adoption” etc.. Some conceptual search solutions will also cluster concepts to give the user the ability to quickly fine-tune their search; for example create a cluster of all dog (animal) references, a cluster for all food related references and so on. Many eDiscovery analytic solutions include this clustering capability.

Predictive coding is a process which includes both automation and human interaction to best produce a results set of potentially responsive documents that trained human reviewers can check.

Predictive coding takes the conceptual search and clustering idea much further than just understanding concepts. A predictive coding solution is “trained” in a very specific manner for each case. For example, the legal team with additional subject matter expertise, manually choose document/records/files that they deem as responsive examples for the particular case and input them to the predictive coding system as examples of content/format which should be found and coded as responsive to the case. Most predictive coding processes include several iterative cycles to fine-tune the example training examples. An iterative cycle would include legal professionals sampling/reviewing those records coded as responsive by the solution and determining if they are truly responsive in the opinion of the human reviewer. If the reviewers find examples of documents that are not deemed responsive, then those documents would then in turn be used to train the solution to disregard or not code as responsive specific content based on the iterative examples. This iterative cycle could be processed several times until the human professionals agree the system has reached the desired level of capability. By the way, this iterative process can and is also used to sample results sets of documents deemed non-responsive to determine if the solution is not finding potentially responsive content. This process is called “Elusion”. Elusion is the process to count the proportion of misses that a system yielded. The proportion of misses, is the proportion of responsive documents that were not marked responsive by the solution. Elusion is the proportion of missed documents that are in fact responsive. This elusion process can also be used in the iterative cycle to further train the system.

The obvious benefit of a predictive coding solution in the eDiscovery process is to dramatically reduce the time spent on legal professionals reading each and every document to determine its responsiveness. A 2012 RAND Institute for Civil Justice report estimated a savings of 80% for the eDiscovery review process (73% of the total cost of eDiscovery) when using a predictive coding solution.

So, to answer the question, conceptual search is an automated information retrieval method which is used to search electronically stored unstructured text for information that is conceptually similar to the information provided in a search query. In other words, the ideasexpressed in the information retrieved in response to a concept search query are relevant to the ideas contained in the text of the query.

Predictive coding is a process (which can include conceptual search) which uses machine learning technologies to categorize (or code) an entire corpus of documents as responsive, non-responsive, or privileged based on human chosen examples used to train the system in an iterative process. These technologies typically rank the documents from most to least likely to be responsive to a specific information request. This ranking can then be used to “cut” or partition the documents into one or more categories, such as potentially responsive or not, in need of further review or not, etc1.

1 Partial definition from the eDiscovery Daily Blog: http://www.ediscoverydaily.com/2010/12/ediscovery-trends-what-the-heck-is-predictive-coding.html

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Early Case Assessment and Concept Search


There has been an ongoing argument as to the validity of concept search verses keyword search in discovery searches. The main arguments I have seen are:

  1. Keyword searches tend to miss relevant documents and are under-inclusive in their search results.
  2. Concept searches tend to produce too many non-responsive documents and are considered over-inclusive in their search results.
  3. The other argument against concept searches for eDiscovery is that concept searches are a “black box” and are therefore very hard to explain to the court as to their validity.

I have not been able to find any cases where the eDiscovery response was conducted via a concept search.

While at LegalTech 2010 in New York, I spoke to several conceptual search/clustering vendors that were positioning conceptual search as the next big thing…that keyword search was falling in favor. I don’t believe that to be the case but I am curious whether conceptual search technology has a future.

I do believe there is an interesting possibility to use conceptual search capabilities in the area of Early Case Assessments (ECA). For ECA, the discoveree wants to “data mine” potentially responsive ESI to determine their going forward strategy; should we settle or should we fight? To make the best decision about legal strategy, I believe having access to the most complete and relevant data set is a top priority. One of the wraps against concept search is it is over-inclusive; a benefit in making sure you have reviewed all potentially responsive ESI when performing ECA.