@inproceedings{5938b98d2d5f4a75a4d34bc2a26f34bb,
title = "Classifying legal norms with active machine learning",
abstract = "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.",
keywords = "Active machine learning, Norm classification, Text mining",
author = "Bernhard Waltl and Johannes Muhr and Ingo Glaser and Georg Bonczek and Elena Scepankova and Florian Matthes",
note = "Publisher Copyright: {\textcopyright} 2017 The authors and IOS Press.; 30th International Conference on Legal Knowledge and Information Systems, JURIX 2017 ; Conference date: 13-12-2017 Through 15-12-2017",
year = "2017",
doi = "10.3233/978-1-61499-838-9-11",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "11--20",
editor = "Adam Wyner and Giovanni Casini",
booktitle = "Legal Knowledge and Information Systems - JURIX 2017",
}