Mining Legal Arguments in Court Decisions
- URL: http://arxiv.org/abs/2208.06178v2
- Date: Wed, 17 May 2023 12:35:36 GMT
- Title: Mining Legal Arguments in Court Decisions
- Authors: Ivan Habernal, Daniel Faber, Nicola Recchia, Sebastian Bretthauer,
Iryna Gurevych, Indra Spiecker genannt D\"ohmann, Christoph Burchard
- Abstract summary: We develop a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights.
Second, we compile and annotate a large corpus of 373 court decisions.
Third, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain.
- Score: 43.09204050756282
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Identifying, classifying, and analyzing arguments in legal discourse has been
a prominent area of research since the inception of the argument mining field.
However, there has been a major discrepancy between the way natural language
processing (NLP) researchers model and annotate arguments in court decisions
and the way legal experts understand and analyze legal argumentation. While
computational approaches typically simplify arguments into generic premises and
claims, arguments in legal research usually exhibit a rich typology that is
important for gaining insights into the particular case and applications of law
in general. We address this problem and make several substantial contributions
to move the field forward. First, we design a new annotation scheme for legal
arguments in proceedings of the European Court of Human Rights (ECHR) that is
deeply rooted in the theory and practice of legal argumentation research.
Second, we compile and annotate a large corpus of 373 court decisions (2.3M
tokens and 15k annotated argument spans). Finally, we train an argument mining
model that outperforms state-of-the-art models in the legal NLP domain and
provide a thorough expert-based evaluation. All datasets and source codes are
available under open lincenses at
https://github.com/trusthlt/mining-legal-arguments.
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