在印尼语推特中对危机事件的语义角色标签进行注释的数据
Amelia Devi Putri Ariyanto1,2, Diana Purwitasari1, Bilqis Amaliah1
1Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
Data in brief
|June 16, 2025
概括
研究人员从Twitter危机数据开发了一个新的印尼语义角色标签 (SRL) 数据集. 这种资源有助于自然语言处理 (NLP) 以加快灾难应对和分析.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 提取信息 提取信息
- 计算语言学 计算语言学
背景情况:
- 社交媒体,特别是推特,提供了实时的危机信息,但其非结构化的性质阻碍了分析.
- 语义角色标签 (SRL) 对于从危机文本中提取关键事件细节 (谁,什么,什么时候,在哪里) 是至关重要的.
- 印尼SRL数据集的有限可用性阻碍了低资源语言的危机信息学NLP开发.
研究的目的:
- 开发一套新的印尼语SRL数据集,专门用于来自Twitter的危机事件.
- 促进创建自动化信息提取模型,用于灾害应对和分析.
- 加强印尼语的NLP能力,专注于危机事件理解.
主要方法:
- 使用与危机相关的关键词从2018-2023年收集的推特数据.
- 预先处理数据以确保危机事件分析的相关性和清洁性.
- 两位专家手动注释了99,206个有SRL和命名实体识别 (NER) 标签的代币,实现了高度的注释者间协议 (科恩斯卡帕>0.90).
主要成果:
- 从Twitter危机数据成功创建了一个高质量的印度尼西亚SRL和NER数据集.
- 数据集包含99,206个注释令牌,通过专家注释和协议证明可靠的数据质量.
- 数据集的结构是为了支持先进的SRL和NER模型的开发.
结论:
- 开发的数据集解决了印尼NLP资源为危机信息学的稀缺问题.
- 该资源将加速用于灾害影响分析和减缓计划的自动化系统的开发.
- 它为印尼语的NLP研究开辟了新的途径,特别是危机期间的实时事件理解.
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