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Interesting article, but it's not really clear to me what the actual application is (and this is coming from someone who started my career in law, and was recen
by avinium 8y ago
Interesting article, but it's not really clear to me what the actual application is (and this is coming from someone who started my career in law, and was recently working on using ML for contract review automation).
As far as I can tell, it's intended to classify each paragraph as one of "facts", "pleas", "grounds", etc. On its own, this doesn't seem particularly useful. I assume it feeds into the broader Doctrine platform (litigation search and case summaries perhaps?). I jumped to their website but couldn't say for sure as I don't speak French.
- arnaudmiribel 8y agoThat's totally right. In our original dataset, 45% of decisions have no explicit titles splitting categories, so it's quite difficult to read them. We classify each paragraph as one of "facts", "pleas", "grounds", etc. in order to generate a table of contents and help users read a court decision more easily. For example, one may want to directly jump to the operative part of the judgement: he just has to click on the "Operative part" of the table of contents, instead of having to read all the decision and guess when that part actually starts. It's indeed intended to improve our users experience on Doctrine's website https://www.doctrine.fr https://www.doctrine.fr (legal search engine).