ECHR Corpus

The ECHR Corpus is a corpus of legal documents for Argument Mining. It consists of 42 human-annotated judgements of European Court of Human Rights (ECHR). The corpus is annotated in terms of three types of argument constituents relevant in argument mining: premise, conclusion, and non-argument parts of the text. Overall, there are 1951 premises and 743 conclusions. Note that some argument constituents could be premises/conclusions for more than one argument, and some constituents are both premises of one argument and conclusion for another one.

The documents were annotated in the Gloss annotation environment developed at the University of Pittsburgh. Gloss is a lightweight tool focused on semantic annotation of textual documents. Through a small number of individual components it supports the whole annotation process, including corpus assembly, type system definition, document annotation, as well as quality control. The system is equipped with simple identity and role management that facilitates basic security as well as the ability of multiple users performing various roles within a project.

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To cite ECHR Corpus paper

Prakash Poudyal, Jaromir Savelka, Aagje Ieven, Marie Francine Moens, Teresa Goncalves and Paulo Quaresma. 2020 ECHR: Legal Corpus for Argument Mining. Proceedings of the 7th Argument Mining Workshop 2020.

Related Papers

  1. Prakash Poudyal, Teresa Gonçalves, and Paulo Quaresma. "Using Clustering Techniques to Identify Arguments in Legal Documents." ASAIL@ ICAIL. 2019.

  2. Prakash Poudyal, Teresa Goncalves, and Paulo Quaresma. "Experiments on identification of argumentative sentences." 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). IEEE, 2016.

  3. Jaromir Savelka and Kevin D. Ashley. "Segmenting US Court Decisions into Functional and Issue Specific Parts." JURIX. 2018.

  4. Jaromír Savelka and Kevin D. Ashley. "Detecting Agent Mentions in US Court Decisions." JURIX. 2017.

  5. Prakash Poudyal. "A Machine Learning Approach to Argument Mining in Legal documents." AI Approaches to the Complexity of Legal Systems. Springer, Cham, 2015. 443-450.

  6. Raquel Mochales, and Marie-Francine Moens. "Argumentation mining." Artificial Intelligence and Law 19.1 (2011): 1-22.

  7. Raquel Mochales and Marie-Francine Moens. "Study on the structure of argumentation in case law." Proceedings of the 2008 Conference on Legal Knowledge and Information Systems. 2008.

  8. Raquel Mochales and Aagje Ieven. "Creating an argumentation corpus: do theories apply to real arguments? A case study on the legal argumentation of the ECHR." Proceedings of the 12th international conference on artificial intelligence and law. 2009.