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--- |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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language: |
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- it |
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--- |
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# Dataset Card for AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge |
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This dataset consists of argumentative components extracted from 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. |
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The proposed tasks consists of three classifications, in the context of argument mining in the legal domain. |
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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. |
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## Dataset Details |
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### Dataset Source |
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- **Repository:** https://github.com/adele-project/AMELIA/ |
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### Dataset Structure |
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The dataset consists of the following columns: |
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- Text: the text of the argumentative component |
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- Document: the document it belongs to |
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- Component: if it is a premise (prem) or a conclusion (conc) |
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- Type: a list value representing the type of a premise; the list contains F for a Factual premise and L for a Legal one. |
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- Scheme: a list value representing the argumentative schemes of a legal premise. The values are: Rule, Prec, Class, Itpr and Princ. |
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- Chain_id: univocal for each document, it specifies the argumentative chain the component belongs to (e.g. A1, A2,..., B1, B2,...) |
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- Id: an univocal numerical id |
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## Citation |
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**BibTeX:** |
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@inproceedings{ |
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author = {Giulia Grundler and |
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Andrea Galassi and |
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Piera Santin and |
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Alessia Fidangeli and |
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Federico Galli and |
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Elena Palmieri and |
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Francesca Lagioia and |
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Giovanni Sartor and |
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Paolo Torroni}, |
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title = {AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge}, |
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booktitle = {Proceedings of CLiC-it 2024: Tenth Italian Conference on Computational Linguistics}, |
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year = {}, |
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doi = {}, |
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} |
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