  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.
