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How would you analyse and organise thousands – or even millions – of documents so they can be found and used as efficiently as possible?
In a large law firm, a typical knowledge management system often accounts for 10,000 to 20,000 documents and general matter documents might number in the millions.
How would you then quickly repeat the exercise to account for documents being edited or added to/removed from such a database?
Artificial intelligence (AI) creates an opportunity to meet such challenges. The process is not, however, as simple as flicking a switch.
Taxonomy - the art of naming and sorting things - might be said to have its roots in ancient history. Egyptian wall paintings dating back to 1500 BC illustrating plants with medicinal properties might be viewed as early examples of basic taxonomy.
Fast-forward 3,500 years and taxonomists have so far managed to describe (ie name) about 1.9 million species of living things on Earth.
Now consider that, over the past 20 years, LexisNexis UK has built a digital database of over eight million legal documents.
In 2013 we were faced with the job of applying a new taxonomy to that entire database.
To illustrate the scale of the task, imagine an entirely manual review process. If each document took six minutes, a project of this size would require more than an average lifetime of non-stop work – 91 years!
While our taxonomy was not so manual, it was based on (manually written) keyword-based rules. A new taxonomy would, therefore, have involved a significant amount of manual input.
We needed a new, more automated, system. However, no such system existed. We would have to build it ourselves.
Despite their names, Thor and Roxie are not superhumans but core components of our homegrown supercomputer – the High Performance Computing Cluster (HPCC). We combined HPCC with (open source) natural language processing (NLP) code to create an AI system for the project.
At this point, we just needed to switch on the AI and sit back, right? Unfortun
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