We’re still on the path of talking about legal, judicial, and general law with specific AI. Today we’re going to talk a little bit about the work of having a machine understand some of these laws yielding an Increasing AI Understanding of Legal Clauses.
Clause extraction is often just looking for a way to segment certain documents, whatever clauses are you extracting from and it has many methods of extracting those. A clause is a self-contained paragraph or a section of text or content on a page, generally, it’s not incredibly hard to pull them out. The difficulty starts happening a little bit more when you’re trying to understand what those clauses are about, sort of classifying them.
There are a lot of possible classifications of these paragraphs or clauses which makes it like it’s that next step beyond just separating them out. If you get into the legal domain or the legal world, basically the language inherently in those clauses is obviously going to be a little more technical.
So there are more caveats and specific understanding required of your machine learning or NLP programs to actually extract. Pulling the paragraphs out is not that hard, but once you have them out, it’s again, understanding them from the legal perspective as to what they’re for.
With this in mind, the FILAC Model was developed as a constraint to a specific domain of law to help structure the information.
There are other techniques especially in neural networks, in deep learning where without supervision so an unsupervised model, you can cluster information, helping group the different clauses and the intent of those in a way that suits your workload better.
So that sort of wraps up us talking about our language models a little bit, talking about clause identification, extraction and classification, and the whole building up of legal AI.