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 this https URL

LexNLP 是一个面向法律和监管文本的自然语言处理和机器学习的开源 Python 包，提供文档分析、信息识别、实体提取、特征转换、无监督 / 监督模型构建等多个功能，其中包含 18 种结构化信息提取以及预训练模型，并可适用于学术研究和工业应用。