nlp-unibo/AMELIA

---
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 = {},
}