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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.
News article revision histories have the potential to give us novel insights across varied fields of linguistics and social sciences. In this work, we present, to our knowledge, the first publicly available dataset of news article revision histories,
In this work, we present a Web-based annotation tool `Relation Triplets Extractor footnote{https://abera87.github.io/annotate/} (RTE) for annotating relation triplets from the text. Relation extraction is an important task for extracting structured i
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language r
We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solvers using derivations, which reflect how an equation system was construc
Curated databases have become important sources of information across scientific disciplines, and due to the manual work of experts, often become important reference works. Features such as provenance tracking, archiving, and data citation are widely