Attorney here just getting into NLP. What should I be doing to take advantage of this intersection? I am going to use this dataset and explore more.

What kind of law do you practice?

[deleted]

Gov. Lit. I’m trying to make the transition to in-house for a corp. while continuing to build my skills and GitHub as a full-stack dev (JS/Python/SQL). The goal is GC and C-Suite for an F500 and leverage my legal, finance, and dev skills into some weird hybrid in the future.

What do I know, but I’d suggest the best thing you can bring to the table is identifying worthwhile problems in law which are solvable with AI.

Too often we have ML people picking problems amenable to ML algorithms but which ultimately don’t create a ton of value, or domain experts picking important problems which are unsolvable with current techniques.

create a lexis-nexis competitor 🤭

I’m not sure if I missed it while skimming the repo and paper but do you have a license on this?

Edit: and I did miss it…in section A.1 they state that it’s under CC-BY-4.0.

Not Expert in law but this dataset has any value for British law?

The subject matter a company such as Everlaw covers might be of interest

repo and paper but do you have

Computational Linguist here, currently getting a PhD in NLP. If you want to get into that area, full-stack development isn't going to help (although it's a cool thing to do on its own if you're interested). Web development and Data Science (ML /DL etc.) are very different thigns. Also, while SQL is helpful in a lot of ML projects, natural language data is unstructured and is not to be stored in SQL databases. Instead, I'd suggest learning the fundamentals of Machine Learning first. Once you're there, you can start specializing in NLp topics. As a lawyer, your strength will probably be understanding the methods enough so you can assess whether or not a certain problem can be solved with NLP. Hope this helps!

Thank you for the advice. I fell into development during the pandemic and it’s been my new area of interest ever since. Being a full-stack dev is just a personal goal of mine. I am planning on learning more about ML in general after I finish reading/following along with the NLTK book, and I will likely take a course. Do you recommend any SPECIFIC materials? I know what’s commonly recommended via wiki and search, but I’m curious to know what you’re using and reading in your program or what you’d recommend in general, personally.

As a domain expert, you’d probably want to focus specifically on feature engineering if you’re looking to continue training the existing model or new models. A lot of it comes down to asking good questions and hypothesis testing informed by knowledge of the law that you already have.

Figuring out how to use those models in real-world applications employs a different skillset, however, and that sounds more like what your original question is asking about. You’d probably get a better sense of this through examples of applications that intro to ML courses reference and surveying ML-driven applications in various industries. Here's a good hands-on resource: https://machinelearningmastery.com/start-here

It is a common law dataset, but it is trained on American M&A concepts, rather than UK.

EE here, 25 years in NLP. Working in Prolog, Datalog, Logica for formal verification. Using NLP to extract facts for verification.

Can you contribute datasets?

That would help so many!

[removed]

Found relevant code at https://github.com/TheAtticusProject/maud + all code implementations here

--

To opt out from receiving code links, DM me