# StateCensusLaws.org: A Web Application for Consuming and Annotating Legal Discourse Learning

arXiv: https://arxiv.org/abs/2104.10263 · HTML: https://mlsanity.com/paper/2104.10263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search](https://mlsanity.com/paper/2005.12668/) (2020)
- [YEDDA: A Lightweight Collaborative Text Span Annotation Tool](https://mlsanity.com/paper/1711.03759/) (2017)
- [Multitask Learning for Class-Imbalanced Discourse Classification](https://mlsanity.com/paper/2101.00389/) (2021)

## Abstract (arXiv)

In this work, we create a web application to highlight the output of NLP models trained to parse and label discourse segments in law text. Our system is built primarily with journalists and legal interpreters in mind, and we focus on state-level law that uses U.S. Census population numbers to allocate resources and organize government. Our system exposes a corpus we collect of 6,000 state-level laws that pertain to the U.S. census, using 25 scrapers we built to crawl state law websites, which we release. We also build a novel, flexible annotation framework that can handle span-tagging and relation tagging on an arbitrary input text document and be embedded simply into any webpage. This framework allows journalists and researchers to add to our annotation database by correcting and tagging new data.