metadata

license: cc-by-4.0
task_categories:
  - text-classification
language:
  - it

Dataset Card for AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge

This dataset consists of argumentative components extracted from 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. The proposed tasks consists of three classifications, in the context of argument mining in the legal domain. 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.

Dataset Details

Dataset Source

Repository: https://github.com/adele-project/AMELIA/

Dataset Structure

The dataset consists of the following columns:

Text: the text of the argumentative component
Document: the document it belongs to
Component: if it is a premise (prem) or a conclusion (conc)
Type: a list value representing the type of a premise; the list contains F for a Factual premise and L for a Legal one.
Scheme: a list value representing the argumentative schemes of a legal premise. The values are: Rule, Prec, Class, Itpr and Princ.
Chain_id: univocal for each document, it specifies the argumentative chain the component belongs to (e.g. A1, A2,..., B1, B2,...)
Id: an univocal numerical id

Citation

BibTeX:

@inproceedings{ author = {Giulia Grundler and Andrea Galassi and Piera Santin and Alessia Fidangeli and Federico Galli and Elena Palmieri and Francesca Lagioia and Giovanni Sartor and Paolo Torroni}, title = {AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge}, booktitle = {Proceedings of CLiC-it 2024: Tenth Italian Conference on Computational Linguistics}, year = {}, doi = {}, }