Learning alignments from legislative discourse
Daniel Kauffman, Foaad Khosmood, Toshihiro Kuboi, Alex Dekhtyar · 2018
In this work, we seek to quantify the extent to which a legislator's spoken language indicates their degree of alignment toward an organization that has a taken a documented position on some legislation. To perform this study, we use a corpus of bill discussion transcripts provided by Digital Democracy1. We then apply proven learning methods in the field of natural language processing to predict alignment scores between each member of the California state legislature and a select set of state-recognized organizations. Our methods surpass established baselines, achieving up to 78% accuracy when predicting these same scores using discourse features.