普什图语攻击性语言检测:一个基准数据集和单语言普什图语BERT
Ijazul Haq1, Weidong Qiu1, Jie Guo1
1School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, Minhang, China.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了普什图攻击性语言数据集 (POLD),用于检测普什图社交媒体中的攻击性内容. 一个巴什图语BERT模型实现了高精度,在这种低资源语言中表现优于其他人工智能方法.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 社交媒体上的冒犯性语言对在线社区构成威胁.
- 在像巴什图语这样的低资源语言中检测有攻击性的内容是一个尚未探索的研究领域.
- 现有的研究主要集中在高资源语言上,对普什图语留下了一个空白.
研究的目的:
- 开发一种人工智能模型,用于自动检测普什图语的攻击性文本.
- 创建一个基准数据集,用于识别普什图语的攻击性语言.
- 评估和比较各种深度学习和转移学习模型来完成这一任务.
主要方法:
- 从Twitter数据开发了帕什托攻击性语言数据集 (POLD).
- 实施和评估深度学习模型 (CNN,RNN) 与静态词嵌入 (Word2Vec,fastText,GloVe).
- 使用XLM-R和一个定制培训的Pashto BERT模型研究了转移学习.
主要成果:
- 与其他评估模型相比,Pashto BERT模型实现了更高的性能.
- 普什图语BERT模型获得了F1得分94.34%,准确率为94.77%.
- 深度学习和转移学习方法在POLD数据集上进行了基准测试.
结论:
- 开发的Pashto BERT模型对于在Pashto中检测有攻击性的文本是有效的.
- 这项研究为一种低资源语言提供了有价值的数据集和高性能模型.
- 在帕什托语中自动检测攻击性内容是可行的,对在线安全至关重要.
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