Machine Learning
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Is linguistic information relevant for the classification of legal texts?
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Automatic classification of provisions in legislative texts
Artificial Intelligence and Law - AI & law in eGovernment and eDemocracy part II
Proceedings of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference
A next step towards automated modelling of sources of law
Proceedings of the 12th International Conference on Artificial Intelligence and Law
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Exploiting Properties of Legislative Texts to Improve Classification Accuracy
Proceedings of the 2009 conference on Legal Knowledge and Information Systems: JURIX 2009: The Twenty-Second Annual Conference
Knowledge acquisition for categorization of legal case reports
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Eunomos, a legal document and knowledge management system to build legal services
AICOL'11 Proceedings of the 25th IVR Congress conference on AI Approaches to the Complexity of Legal Systems: models and ethical challenges for legal systems, legal language and legal ontologies, argumentation and software agents
A system for classifying multi-label text into EuroVoc
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (90%) as the pattern based one, but seems to generalize worse to new laws. Given these results, the pattern based approach is to be preferred since its reasons for classification are clear and can be used for further modelling of the content of the sentences.