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构建和评估ArabStanceX:一个社交媒体数据集用于阿拉伯语姿势检测

Ali Alkhathlan¹, Faris Alahmadi¹, Faris Kateb²

¹Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Frontiers in artificial intelligence

|July 4, 2025

概括

研究人员开发了ArabicStanceX,这是一个用于在社交媒体上检测阿拉伯语姿势的大型数据集. 这个资源有助于阿拉伯自然语言处理 (NLP) 的进步,改进了意见分析.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
计算社会科学计算社会科学

背景情况:

社交媒体显著影响公众论,使阿拉伯语立场检测至关重要.
有限的数据集阻碍了阿拉伯语NLP研究的进展,以了解社交媒体话语.

研究的目的:

介绍ArabStanceX,这是一个用于阿拉伯语姿势检测的全面数据集.
通过为分析社交媒体意见提供有价值的资源,推进阿拉伯语NLP的研究.
评估基于变压器的模型对多主题立场检测的有效性.

主要方法:

收集并策划了ArabicStanceX,这是一个包含17个主题的14477条推文的数据集.
采用MARBERTv2变压器模型用于使用多主题单一模型 (MTSM) 策略进行位置检测.
对模型适应性的零射击和少数射击学习方法进行了研究.

主要成果:

在"赞成"/"反对"立场方面获得了F1的0.74分,在使用MARBERTv2.2时获得了整体0.67分.
识别了分类中立立场和概括到未见的主题的挑战.
通过零射击和少数射击学习证明了模型适应新环境的适应性.

结论:

阿拉伯语StanceX对阿拉伯语NLP做出了重大贡献,促进了姿势检测研究.
该研究提供了关于阿拉伯语姿势检测当前模型能力和局限性的见解.
公开可用的数据集和研究结果为未来跨主题和低资源立场检测研究铺平了道路.

关键词:

阿拉伯语的NLP语言.阿拉伯语阿拉伯语阿拉伯语阿拉伯语论挖掘是如何挖掘一个人的意见.社交媒体分析分析位置检测检测位置检测

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