A maximum entropy approach to natural language processing
Computational Linguistics
ARGUER: using argument schemas for argument detection and rebuttal in dialogs
UM '99 Proceedings of the seventh international conference on User modeling
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic detection of arguments in legal texts
Proceedings of the 11th international conference on Artificial intelligence and law
Automatic Argumentation Detection and its Role in Law and the Semantic Web
Proceedings of the 2009 conference on Law, Ontologies and the Semantic Web: Channelling the Legal Information Flood
Argumentation mining: the detection, classification and structure of arguments in text
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Artificial Intelligence and Law
Using Argumentation Schemes for Argument Extraction: A Bottom-Up Method
International Journal of Cognitive Informatics and Natural Intelligence
Using Argumentation Schemes for Argument Extraction: A Bottom-Up Method
International Journal of Cognitive Informatics and Natural Intelligence
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We report the results of experiments which prove that the analysis of relations between sentences increase the accuracy in the automatic detection of arguments in legal cases. We treat the search of arguments as a classification problem. Our corpus is a human-annotated and automatically-extracted test set from a collection of legal cases of the European Court of Human Rights. We obtain an increment around 8% in the general accuracy compared to previous experiments due to the addition of new features that study the relations between the text sentences.