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Toward Extracting Information from Public Health Statutes using Text Classification Machine Learning
Matthias Grabmair, Kevin D. Ashley, Rebecca Hwa, Patricia M. Sweeney
73 - 82
10.3233/978-1-60750-981-3-73
Frontiers in Artificial Intelligence and Applications
Volume 235: Legal Knowledge and Information Systems
Abstract

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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