vadis/sv-ident

SV-Ident数据集包含来自社会科学出版物的4,248个句子，支持变量检测和变量消歧任务。数据集的文本为英文和德文，由专家生成和注释。数据集主要用于文本分类任务，特别是多标签分类和语义相似性分类。

The SV-Ident dataset consists of 4,248 sentences sourced from social science publications, supporting variable detection and variable disambiguation tasks. The texts in the dataset are in both English and German, and were generated and annotated by experts. It is primarily used for text classification tasks, particularly multi-label classification and semantic similarity classification.

原始信息汇总

数据集概述

数据集名称

• 名称: SV-Ident
• 别名: 无

数据集基本信息

• 语言: 英语 (en), 德语 (de)
• 许可证: MIT
• 多语言性: 多语言
• 数据集大小: 1K<n<10K
• 源数据: 原始数据

数据集任务

• 任务类别: 文本分类
• 具体任务:
  • 多标签分类
  • 语义相似度分类

数据集结构

• 数据实例: 包含句子、是否包含变量、提及的变量、研究数据ID、文档ID、唯一ID和语言等字段。
• 数据字段:
  • sentence: 文本实例，可能包含变量提及。
  • is_variable: 标签，指示文本实例是否包含变量提及。
  • variable: 文本实例中提及的变量。
  • research_data: 与实例相关的研究数据ID。
  • doc_id: 源文档的ID。
  • uuid: 实例的唯一ID。
  • lang: 句子的语言。
• 数据分割:
  • 训练集: 3,823 句
  • 验证集: 425 句

数据集创建

• 创建理由: 由VADIS项目创建，用于“社会科学出版物中的调查变量识别”共享任务。
• 源数据: 来自GESIS的未处理数据。
• 注释者: 两名专家注释者。
• 个人敏感信息: 数据集中不包含个人或敏感信息。

许可证信息

• 数据源自社会科学开放存取资源库(SSOAR)，遵循相应的许可证。

引用信息

@inproceedings{tsereteli-etal-2022-overview, title = "Overview of the {SV}-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications", author = "Tsereteli, Tornike and Kartal, Yavuz Selim and Ponzetto, Simone Paolo and Zielinski, Andrea and Eckert, Kai and Mayr, Philipp", booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.sdp-1.29", pages = "229--246", abstract = "In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual sentences from scholarly documents in full text. Two teams made a total of 9 submissions to the shared task leaderboard. While none of the teams improve on the baseline systems, we still draw insights from their submissions. Furthermore, we provide a detailed evaluation. Data and baselines for our shared task are freely available at url{https://github.com/vadis-project/sv-ident}.", }