DFKI-SLT/SemEval2018_Task7
收藏资源简介：
SemEval2018Task7是一个专注于科学论文中语义关系抽取和分类的数据集。该数据集包含三个子任务，旨在比较和量化不同预处理步骤对关系分类结果的影响。数据集的语言为英语，包含1K到10K之间的数据实例。数据集的标注由专家生成，适用于从领域语料库中提取专门知识的研究人员，特别是科学或生物医学信息提取领域。
SemEval2018 Task 7 is a dataset dedicated to semantic relation extraction and classification in scientific papers. This dataset includes three subtasks, aiming to compare and quantify the impacts of different preprocessing steps on relation classification results. The dataset is in English, with the number of data instances ranging from 1,000 to 10,000. Its annotations are generated by domain experts, and it is suitable for researchers who extract specialized knowledge from domain corpora, especially those in the fields of scientific or biomedical information extraction.
数据集概述
数据集基本信息
- 数据集名称: SemEval2018Task7
- 数据集描述: 该数据集用于描述科学论文中的语义关系提取和分类。
- 语言: 英语
- 数据集大小: 1K<n<10K
- 任务类别: 文本分类
- 任务ID: entity-linking-classification
- 标签: 关系分类, 关系提取, 科学论文, 研究论文
数据集结构
数据实例
Subtask_1.1
- 数据文件大小: 714 KB
- 数据字段:
id: 实例ID，字符串类型title: 标题，字符串类型abstract: 摘要，字符串类型entities: 实体列表，包含实体ID、起始和结束字符位置relations: 关系列表，包含关系标签、实体ID和是否可逆
Subtask_1.2
- 数据文件大小: 1.00 MB
- 数据字段:
id: 实例ID，字符串类型title: 标题，字符串类型abstract: 摘要，字符串类型entities: 实体列表，包含实体ID、起始和结束字符位置relations: 关系列表，包含关系标签、实体ID和是否可逆
数据分割
| 任务 | 类型 | 训练集 | 测试集 |
|---|---|---|---|
| Subtask_1.1 | 文本 | 2807 | 3326 |
| 关系 | 1228 | 1248 | |
| Subtask_1.2 | 文本 | 1196 | 1193 |
| 关系 | 335 | 355 |
数据集创建
来源数据
初始数据收集和标准化
- 信息缺失
源语言生产者
- 信息缺失
注释
注释过程
- 信息缺失
注释者
- 信息缺失
个人和敏感信息
- 信息缺失
使用数据注意事项
数据集的社会影响
- 信息缺失
偏见讨论
- 信息缺失
其他已知限制
- 信息缺失
附加信息
数据集管理员
- 信息缺失
许可信息
- 信息缺失
引用信息
@inproceedings{gabor-etal-2018-semeval, title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers", author = {G{a}bor, Kata and Buscaldi, Davide and Schumann, Anne-Kathrin and QasemiZadeh, Behrang and Zargayouna, Ha{"i}fa and Charnois, Thierry}, booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S18-1111", doi = "10.18653/v1/S18-1111", pages = "679--688", abstract = "This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.", }
贡献者
- 信息缺失
