Predicting and Understanding Law-Making with Machine Learning

Report number arXiv:1607.02109 Title Predicting and Understanding Law-Making with Machine Learning Author(s) Nay, John J Imprint 07 Jul 2016 Note Comments: For real-time predictions of all current Congressional bills see http://predictgov.com/projects/ Subject category Computing and Computers Abstract Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a semantic-laden vector space. This language representation enables our…


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