In this paper we mine statutory texts for highly-specific functional information using NLP techniques and a supervised ML approach. We focus on regulatory provisions from multiple state jurisdictions (Pennsylvania and Florida), all dealing with the same general topic (i.e., public health system emergency preparedness and response). While the number of annotated provisions from any one jurisdiction is not large, we are investigating whether one can improve classification performance on one jurisdiction's statutory texts by including other jurisdictions' annotated statutory texts dealing with the same general topic. Our experiments suggest that data from one jurisdiction can be used to boost the performance of the classifiers trained for different jurisdictions.