Daniel Argyle

TL;DR: This article proposed a novel neural method for encoding documents alongside additional metadata, achieving an average of a 4% boost in accuracy over the previous state-of-the-art state of the art.

TL;DR: This work utilizes the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build predictive models, allowing a comparison of what factors are important to the lawmaking process and shows that these signals hold complementary predictive power.

TL;DR: In this paper, a system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy.

TL;DR: In this article, the authors present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. They utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build their predictive models, allowing a comparison of the factors that are important to the lawmaking process.