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DFKI-SLT/tacred|关系抽取数据集|知识库构建数据集
收藏hugging_face2024-05-15 更新2024-03-04 收录
下载链接：
https://hf-mirror.com/datasets/DFKI-SLT/tacred
下载链接
链接失效反馈资源简介：
The TAC Relation Extraction Dataset (TACRED)是一个大规模的关系抽取数据集，包含106,264个示例，这些示例构建在新聞稿和网络文本上，源自年度TAC知识库填充（TAC KBP）挑战的语料库。TACRED涵盖了TAC KBP挑战中使用的41种关系类型（例如per:schools_attended和org:members），如果未定义关系，则标记为no_relation。这些示例是通过结合TAC KBP挑战中可用的人工标注和众包创建的。
提供机构：
DFKI-SLT
原始信息汇总
数据集概述
数据集名称
- 名称: The TAC Relation Extraction Dataset, TACRED Revisited and Re-TACRED
- 别名: tacred
数据集基本信息
- 语言: 英语
- 许可证: 其他
- 多语言性: 单语
- 大小类别: 100K<n<1M
- 源数据集: 扩展自其他数据集
- 标签创建者: 众包和专家生成
- 任务类别: 文本分类
- 任务ID: 多类分类
数据集详细描述
- 概述: TACRED是一个大规模的关系抽取数据集，包含106,264个例子，构建于TAC知识库填充挑战所使用的年份新闻和网络文本之上。数据集涵盖41种关系类型，或标记为no_relation。
- 版本: 数据集提供三个版本：原始版本、TACRED Revisited和Re-TACRED。
数据集结构
- 数据实例: 每个实例包括多个字段，如ID、文档ID、关系标签、实体位置等。
- 数据字段: 包括id、docid、token、relation等，详细描述了文本的结构和内容。
- 数据分割: 数据集根据TAC KBP挑战的年份进行分割，以减少偏差。
数据集创建
- 注释过程: 结合TAC KBP挑战的人工注释和众包注释，确保模型训练不偏向于预测错误正例。
- 许可证: 数据集通过Linguistic Data Consortium发布，需要LDC许可证。
使用数据集的注意事项
- 许可证: 使用数据集需遵守LDC许可证，LDC成员可免费访问，非成员需支付$25。
- 引用信息: 引用原始数据集和修订版本时，需遵循相应的引用格式。
数据集版本信息
- 原始版本: 由Zhang等人在2017年发布。
- TACRED Revisited: 由Alt等人在2020年发布，提供了标签修正。
- Re-TACRED: 由Stoica等人在2021年发布，提供了重新标记和修剪的版本。
AI搜集汇总
数据集介绍
构建方式
TACRED数据集的构建基于新闻电讯和网络文本，结合了TAC KBP挑战中可用的人类注释和众包技术。该数据集包含106,264个示例，涵盖了41种关系类型，或标记为无关系。构建过程中，特别注重对无关系样本的充分标注，以确保模型不会对现实世界文本中的假阳性产生偏见。
特点
TACRED数据集的特点在于其大规模的样本量、多样化的关系类型以及结合了专家和众包的注释方式。数据集采用分层划分，以减少数据偏差，并提供了原始、修订和重标记三个版本，以适应不同的研究需求。此外，数据集以英语为语言，遵循LDC许可。
使用方法
使用TACRED数据集时，用户需从Linguistic Data Consortium获取数据。数据集支持关系分类任务，并可通过HuggingFace的load_dataset方法加载，选择特定版本（'original'、'revisited'或're-tacred'）进行使用。用户在获取和使用数据时应遵守相应的版权和许可协议。
背景与挑战
背景概述
TACRED数据集，全称为The TAC Relation Extraction Dataset，是自然语言处理领域中关系提取任务的一个重要数据集。该数据集创建于2017年，由斯坦福大学的研究团队开发，旨在为关系提取任务提供大规模的标注数据。TACRED基于每年举办的TAC KBP挑战赛的语料库构建，包含了106,264个实例，涵盖了41种关系类型。数据集结合了来自TAC KBP挑战赛的人类标注和众包方式生成的标注，对自然语言处理领域的研究具有重要的推动作用。
当前挑战
在TACRED数据集的研究与使用过程中，面临的挑战主要包括：1) 数据集中关系类型的多样性和复杂性，为关系分类任务带来了挑战；2) 数据集构建过程中，如何确保标注质量，减少众包方式可能引入的误差；3) 数据集可能存在的偏差和局限性，例如，数据集中79.5%的例子被标记为无关系，可能导致模型对真实世界文本中的关系预测过于保守；4) 数据集的规模和结构可能对某些应用场景下的模型训练和推理效率产生影响。
常用场景
经典使用场景
在自然语言处理领域，TACRED数据集的经典使用场景主要在于关系提取任务，该任务旨在识别文本中实体之间的语义关系。TACRED以其丰富的关系类型和标注质量，为研究者提供了一个理想的训练和评估平台，使得模型能够准确捕捉如人物组织关系、地理位置关系等多种类型的信息。
衍生相关工作
基于TACRED数据集，学术界衍生出了一系列相关工作，包括对数据集本身的改进，如TACRED Revisited和Re-TACRED，它们分别对原始数据集进行了重新评估和标签修正，提高了数据集的质量。此外，还有许多研究工作利用TACRED数据集在关系提取任务上取得了显著成果，推动了关系提取领域的发展。
数据集最近研究
最新研究方向
TACRED数据集作为关系提取领域的重要资源，近期研究主要聚焦于提升模型对细粒度关系类型的识别能力，以及对无关系实例的准确判断。学者们通过引入位置感知注意力机制、监督数据增强等方法，不断优化模型性能。同时，针对TACRED数据集的修订版本，如TACRED Revisited和Re-TACRED，研究社区正致力于评估不同版本数据集对模型性能的影响，以及它们在促进公平、减少偏差方面的作用。这些研究不仅推动了关系提取任务的进展，也为自然语言处理领域提供了宝贵的数据资源和评估基准。
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