This paper presents preliminary results in extracting semantic information from US state public health legislative provisions using natural language processing techniques and machine learning classifiers. Challenges in the density and distribution of the data as well as the structure of the prediction task are described. Decision tree models trained on a unigram representation with TFIDF measures in most cases outperform the baselines by varying margins, leaving room for further improvement.
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