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Kira Systems Blog

What Is The Difference Between Contract Analysis And eDiscovery Software?  – Contract Review Software Buyer's Guide Part XV

Posted on March 31, 2016 by Noah Waisberg

IconSet1-67.pngOver the past decade, the demand for eDiscovery software has grown dramatically, and there is now a multi-billion dollar market for it. As such, a lot of legal industry people are familiar with eDiscovery software. Contract analysis software has been in the midst of its own little boomlet. New companies keep entering the market, and at least a couple businesses in our space are making money. If you are more familiar with eDiscovery than contract analysis software, this post is for you.

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Non-Standard Contract Clause Detection is Easy to Build, Hard to Get Right — Contract Review Software Buyer's Guide Part XIV

Posted on September 14, 2014 by Noah Waisberg

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Do-It-Yourself Instructions on How to Add Non-Standard Clause Detection to Your Contract Metadata Extraction System — Contract Review Software Buyer's Guide Part XIII

Posted on July 30, 2014 by Noah Waisberg

 

Spot the difference.png Spotting outlying contract provisions isn't as much fun as finding the 16 differences between these two images. Fortunately, simple software can help on the contract side.
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Do-It-Yourself Instructions on How to Build an Automated Contract Provision Extraction System! — Contract Review Software Buyer's Guide Part XII

Posted on July 16, 2014 by Noah Waisberg

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Further Information on Why Who Instructs An Automated Contract Provision Extraction System Matters – Contract Review Software Buyer’s Guide Part XI

Posted on June 1, 2014 by Noah Waisberg

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Garbage In, Garbage Out: Why Who Instructs An Automated Contract Provision Extraction System Matters – Contract Review Software Buyer’s Guide Part X

Posted on May 5, 2014 by Noah Waisberg

 

Good way to build accurate contract provision extraction models?
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Machine Learning-Based Contract Provision Extraction on Poor Quality Scans – Contract Review Software Buyer’s Guide IX

Posted on March 19, 2014 by Noah Waisberg

tl;dr: ML-based contract provision models can be robust enough to work on poor quality scans and unfamiliar agreements, includes video showing this.

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Machine Learning Based Automated Contract Provision Extraction – Contract Review Software Buyer’s Guide Part VIII

Posted on January 31, 2014 by Noah Waisberg

tl;dr: Put contract provision examples in, get provision models out.

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Foundations of Machine Learning-Based Contract Review Software – Contract Review Software Buyer’s Guide Part VII

Posted on December 14, 2013 by Noah Waisberg

The past several posts in the Contract Review Software Buyer’s Guide have gone into details of how manual rule and comparison powered systems actually find contract provisions, and included a case study on a well-funded vendor's experience with manual rules. Manual rule (aka, keyword) and comparison automated contract abstraction systems are appealing to vendors because they are technically easy to build and augment with new provision models. Adding new provisions can be as simple as typing a few regular expression or Boolean search strings. The problem is that keyword and comparison systems are unlikely to work well on unfamiliar agreements and poor quality scans. And most large scale contract review work—whether for legal due diligence or contract management database population—is done on scanned copies of unfamiliar agreements.

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Comparison- and Header Detection-Based Automated Contract Provision Extraction – Contract Review Software Buyer’s Guide Part VI

Posted on November 1, 2013 by Noah Waisberg

The two previous installments of the Contract Review Software Buyer’s Guide covered how manual-rule based contract provision extraction systems—while relatively easy to set up and add provisions to—underperform on agreements and provisions that are not identical or very similar to ones they were built to review ("unfamiliar documents"). Most agreements reviewed in due diligence or contract management projects are unfamiliar documents. The series is now moving on to other methods of automated provision detection. This post will cover comparison- and header detection-based approaches to contract provision extraction. The next post will cover machine learning-based systems. Like manual rules-based systems, comparison- and header detection-based methods are technically easy to set up but underperform on unfamiliar documents. We suspect they are little used on their own; our guess is that only one company in the broader contract review software market has built their system on these.

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