Transfer of predictive models for classification of statutory texts in multi-jurisdictional settings

Abstract

In this paper we use statistical machine learning to classify statutory texts in terms of highly specific functional categories. We focus on regulatory provisions from multiple US state jurisdictions, all dealing with the same general topic of public health system emergency preparedness and response. In prior work we have established that one can improve classification performance on one jurisdiction's statutory texts using texts from another jurisdiction. Here we describe a framework facilitating transfer of predictive models for classification of statutory texts among multiple state jurisdictions. Our results show that the classification performance improves as we employ an increasing number of models trained on data coming from different states.