Abstract
United States Supreme court transcripts are dissected and analyzed in a neural network to discover if the selected featurization reveals any meaningful patterns to determine case outcomes. The vector embedding representations of the courtcase transcripts were not distinguishable and resulted in the equivalent of random guessing in the recurrent neural network as instantiated.
Type