Classifying Legal Norms with Active Machine Learning

This paper describes an extended machine learning approach to classify legal norms in German statutory texts. We implemented an active machine learning (AML) framework based on open-source software. Within the paper we discuss different query strategies to optimize the selection of instances during the learning phase to decrease the required training data. The approach was evaluated within the domain of tenancy law. Thereby, we manually labeled the 532 sentences into eight different functional types and achieved an average F1 score of 0.74. Comparing three different classifiers and four query strategies the classification performance F1 varies from 0.60 to 0.93. We could show that in norm classification tasks AML is more efficient than conventional supervised machine learning approaches.