# LexNLP: Natural language processing and information extraction for legal and regulatory texts

arXiv: https://arxiv.org/abs/1806.03688 · HTML: https://mlsanity.com/paper/1806.03688/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2018.

## Built on by (most cited first, 28 of 28)

- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services](https://mlsanity.com/paper/2309.11325/) (2023)
- [Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://mlsanity.com/paper/2207.00220/) (2022)
- [Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities](https://mlsanity.com/paper/2206.10883/) (2022)
- [Natural Language Processing in the Legal Domain](https://mlsanity.com/paper/2302.12039/) (2023)
- [LEVEN: A Large-Scale Chinese Legal Event Detection Dataset](https://mlsanity.com/paper/2203.08556/) (2022)
- [Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction](https://mlsanity.com/paper/2502.06882/) (2025)
- [The Law and NLP: Bridging Disciplinary Disconnects](https://mlsanity.com/paper/2310.14346/) (2023)
- [Agent-Specific Deontic Modality Detection in Legal Language](https://mlsanity.com/paper/2211.12752/) (2022)
- [Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners](https://mlsanity.com/paper/2305.15533/) (2023)
- [What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions](https://mlsanity.com/paper/2212.09825/) (2022)
- [Logic Rules as Explanations for Legal Case Retrieval](https://mlsanity.com/paper/2403.01457/) (2024)
- [LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2410.12064/) (2024)
- [Automate or Assist? The Role of Computational Models in Identifying Gendered Discourse in US Capital Trial Transcripts](https://mlsanity.com/paper/2407.12500/) (2024)
- [Legal Evalutions and Challenges of Large Language Models](https://mlsanity.com/paper/2411.10137/) (2024)
- [The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers](https://mlsanity.com/paper/2401.01537/) (2024)
- [Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents](https://mlsanity.com/paper/2201.05658/) (2022)
- [Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law](https://mlsanity.com/paper/2308.11531/) (2023)
- [LegalViz: Legal Text Visualization by Text To Diagram Generation](https://mlsanity.com/paper/2502.06147/) (2025)
- [ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation](https://mlsanity.com/paper/2508.17234/) (2025)
- [Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering](https://mlsanity.com/paper/2605.15362/) (2026)
- [ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development](https://mlsanity.com/paper/2305.01145/) (2023)
- [An Automated LLM-based Pipeline for Asset-Level Database Creation to Assess Deforestation Impact](https://mlsanity.com/paper/2505.05494/) (2025)
- [Chinese Labor Law Large Language Model Benchmark](https://mlsanity.com/paper/2601.09972/) (2026)
- [LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw using LLMs](https://mlsanity.com/paper/2603.08286/) (2026)
- [Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions](https://mlsanity.com/paper/2607.23888/) (2026)
- [RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs](https://mlsanity.com/paper/2610.15103/) (2026)

## Abstract (arXiv)

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.