Yesterday we had a great first session of my course – Legal Tech & Informatics — the Innovation, Law and Tech – Executive LLM Program at UToronto Law ! #LegalTech #LegalInnovation #LegalData
It was a great pleasure to participate in the 2018 RSG Financial Times Innovative Lawyers Conference in London !
Our next paper — OpenEDGAR – Open Source Software for SEC Edgar Analysis is now available. This paper explores a range of #OpenSource tools we have developed to explore the EDGAR system operated by the US Securities and Exchange Commission (SEC). While a range of more sophisticated extraction and clause classification protocols can be developed leveraging LexNLP and other open and closed source tools, we provide some very simple code examples as an illustrative starting point.
Click here for Paper: < SSRN > < arXiv >
Access Codebase Here: < Github >
Abstract: OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License at https://github.com/LexPredict/openedgar
Tickets are FREE but registration is required — – http://blocklegaltech.com/
#BlockChain #CryptoInfrastructure #FinTech #LegalTech #ICO
Paper Abstract – LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text. The package includes functionality to (i) segment documents, (ii) identify key text such as titles and section headings, (iii) extract over eighteen types of structured information like distances and dates, (iv) extract named entities such as companies and geopolitical entities, (v) transform text into features for model training, and (vi) build unsupervised and supervised models such as word embedding or tagging models. LexNLP includes pre-trained models based on thousands of unit tests drawn from real documents available from the SEC EDGAR database as well as various judicial and regulatory proceedings. LexNLP is designed for use in both academic research and industrial applications, and is distributed at https://github.com/LexPredict/lexpredict-lexnlp
Academic Tour Continues – tomorrow I will be giving a talk at Bar Ilan University here in Tel Aviv at their Law & Big Data Workshop – it is looks like an good agenda with proper scientific papers with technical results / or discussions about methodology. #LegalScience #LegalData #LegalInformatics
Today I am UConn Law speaking at a Conference entitled – Evaluating Litigation Risk in the 21st Century. Thanks to Alexandra Lahav and the UConn Insurance Law Center for hosting me today!
CLOC Panel – Legal AI in Real Life — (Cisco, Liberty Mutual, Spotify) (I was filling in for Julian T. from Google) — I discussed the contract analytics project we are undertaking with Cisco / Elevate and other applied A.I. / Analytics Projects that the LexPredict Team is undertaking with corporate legal departments !
Yesterday I had the pleasure of participating in the Mexico Legal Summit. The event was hosted at Deloitte Legal in Mexico City and attracted nearly 200 attendees. #LegalInnovation #LegalTech #NewLaw
I am looking forward to speaking on the panel – Artificial Intelligence & Machine Learning Session at The 22nd Annual Corporate Counsel Institute (2018) – Georgetown Law.