  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.
