@inproceedings{grundler-etal-2022-detecting,
title = "Detecting Arguments in {CJEU} Decisions on Fiscal State Aid",
author = "Grundler, Giulia and
Santin, Piera and
Galassi, Andrea and
Galli, Federico and
Godano, Francesco and
Lagioia, Francesca and
Palmieri, Elena and
Ruggeri, Federico and
Sartor, Giovanni and
Torroni, Paolo",
editor = "Lapesa, Gabriella and
Schneider, Jodi and
Jo, Yohan and
Saha, Sougata",
booktitle = "Proceedings of the 9th Workshop on Argument Mining",
month = oct,
year = "2022",
address = "Online and in Gyeongju, Republic of Korea",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2022.argmining-1.14",
pages = "143--157",
abstract = "The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.",
}
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<abstract>The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.</abstract>
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%0 Conference Proceedings
%T Detecting Arguments in CJEU Decisions on Fiscal State Aid
%A Grundler, Giulia
%A Santin, Piera
%A Galassi, Andrea
%A Galli, Federico
%A Godano, Francesco
%A Lagioia, Francesca
%A Palmieri, Elena
%A Ruggeri, Federico
%A Sartor, Giovanni
%A Torroni, Paolo
%Y Lapesa, Gabriella
%Y Schneider, Jodi
%Y Jo, Yohan
%Y Saha, Sougata
%S Proceedings of the 9th Workshop on Argument Mining
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Online and in Gyeongju, Republic of Korea
%F grundler-etal-2022-detecting
%X The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.
%U https://aclanthology.org/2022.argmining-1.14
%P 143-157
Markdown (Informal)
[Detecting Arguments in CJEU Decisions on Fiscal State Aid](https://aclanthology.org/2022.argmining-1.14) (Grundler et al., ArgMining 2022)
ACL
- Giulia Grundler, Piera Santin, Andrea Galassi, Federico Galli, Francesco Godano, Francesca Lagioia, Elena Palmieri, Federico Ruggeri, Giovanni Sartor, and Paolo Torroni. 2022. Detecting Arguments in CJEU Decisions on Fiscal State Aid. In Proceedings of the 9th Workshop on Argument Mining, pages 143–157, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.