Habernal, Ivan ; Faber, Daniel ; Recchia, Nicola ; Bretthauer, Sebastian ; Gurevych, Iryna ; Spiecker genannt Döhmann, Indra ; Burchard, Christoph (2023)
Mining legal arguments in court decisions.
In: Artificial Intelligence and Law, 2023
doi: 10.1007/s10506-023-09361-y
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel | ||||
|---|---|---|---|---|---|
| Erschienen: | 2023 | ||||
| Autor(en): | Habernal, Ivan ; Faber, Daniel ; Recchia, Nicola ; Bretthauer, Sebastian ; Gurevych, Iryna ; Spiecker genannt Döhmann, Indra ; Burchard, Christoph | ||||
| Art des Eintrags: | Bibliographie | ||||
| Titel: | Mining legal arguments in court decisions | ||||
| Sprache: | Englisch | ||||
| Publikationsjahr: | 23 Juni 2023 | ||||
| Verlag: | Springer | ||||
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Artificial Intelligence and Law | ||||
| Jahrgang/Volume einer Zeitschrift: | 2023 | ||||
| DOI: | 10.1007/s10506-023-09361-y | ||||
| Zugehörige Links: | |||||
| Kurzbeschreibung (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. |
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| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
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| Hinterlegungsdatum: | 06 Jul 2023 07:08 | ||||
| Letzte Änderung: | 07 Jul 2023 08:12 | ||||
| PPN: | 509436188 | ||||
| Export: | |||||
| Suche nach Titel in: | TUfind oder in Google | ||||
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