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 = {}, }许可证：CC BY 4.0（知识共享署名4.0国际许可协议） 任务类别： - 文本分类 语言： - 意大利语 # AMELIA数据集卡片——意大利语法律文档论证挖掘（Argument Mining）评估：CALAMITA挑战赛 本数据集包含从225份意大利增值税（Value Added Tax）相关判决中提取的论证成分（argumentative component），并已完成标注以识别并分类论证类文本。 本次提出的任务面向法律领域的论证挖掘场景，包含三项分类子任务：第一项任务的目标是将每个论证成分划分为前提（premise）或结论（conclusion）；第二项与第三项任务则旨在对前提类型进行分类：分别为区分法律前提与事实前提，以及识别其对应的论证模式（argumentation scheme）。 ## 数据集详情 ### 数据集来源 - **存储仓库**：https://github.com/adele-project/AMELIA/ ### 数据集结构 本数据集包含以下字段： - Text：论证成分的文本内容 - Document：该论证成分所属的源文档 - Component：标注该成分属于前提（缩写为prem）还是结论（缩写为conc） - Type：表示前提类型的列表型字段，列表中包含`F`时代表事实前提，包含`L`时代表法律前提 - Scheme：表示法律前提论证模式的列表型字段，可选值包括：Rule（规则）、Prec（先例）、Class（分类）、Itpr、Princ（原则） - Chain_id：单文档内唯一标识符，用于指定该论证成分所属的论证链条（例如A1、A2……、B1、B2……） - Id：全局唯一的数值型标识符 ## 参考文献（BibTeX格式） @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 = {}, }