Elena Palmieri


2024

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AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge
Giulia Grundler | Andrea Galassi | Piera Santin | Alessia Fidelangeli | Federico Galli | Elena Palmieri | Francesca Lagioia | Giovanni Sartor | Paolo Torroni
Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)

This challenge consists of three classification tasks, in the context of argument mining in the legal domain. The tasks are based on a dataset of 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. The objective of the first task is to classify each argumentative component as premise or conclusion, while the second and third tasks aim at classifying the type of premise: legal vs factual, and its corresponding argumentation scheme. The classes are highly unbalanced, hence evaluation is based on the macro F1 score.

2022

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Detecting Arguments in CJEU Decisions on Fiscal State Aid
Giulia Grundler | Piera Santin | Andrea Galassi | Federico Galli | Francesco Godano | Francesca Lagioia | Elena Palmieri | Federico Ruggeri | Giovanni Sartor | Paolo Torroni
Proceedings of the 9th Workshop on Argument Mining

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.