从乌尔都语数据中检测威胁语言,使用深度序列模型
Ashraf Ullah1, Khair Ullah Khan1, Aurangzeb Khan1
1Department of Computer Science, University of Science & Technology Bannu, Bannu, Khyber Pakhtunkhwa, Pakistan.
PloS one
|June 6, 2024
概括
本研究介绍了乌尔都语语言处理工具,包括停止单词列表和停止字典,以改善社交媒体威胁检测. 开发的系统实现了82%的准确性,超过了现有方法.
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
- 社交媒体分析 社交媒体分析
背景情况:
- 由于缺乏专门的图书馆,乌尔都语语言处理 (ULP) 在社交媒体上面临挑战.
- 乌尔都语数据分析的现有方法受到全面的在线乌尔都语词汇库缺失的阻碍.
- 高效的乌尔都文本预处理对于在Twitter和Facebook等平台上准确识别威胁至关重要.
研究的目的:
- 开发必要的乌尔都语语言处理 (ULP) 资源,包括停止单词列表和停止词典.
- 为了实现乌尔都文本的高效数据预处理,与英语语言标准相提并论.
- 通过使用先进的机器学习模型,提高乌尔都语社交媒体数据中威胁检测的准确性.
主要方法:
- 创建乌尔都语语言词汇,包括一个停止单词列表和一个源词典.
- 实施数据清理和阻断技术,以减少输入大小和消除乌尔都语句中的噪音.
- 在预处理的乌尔都语数据上使用长短期记忆 (LSTM) 单元进行深度序列模型的训练和评估.
主要成果:
- 开发的乌尔都语预处理工具有效地减少了句子输入大小,并删除了冗余信息.
- 长短期记忆 (LSTM) 模型在处理的乌尔都语社交媒体数据上实现了82%的预测准确度.
- 与现有的乌尔都语语言处理和威胁检测方法相比,拟议的方法表现优越.
结论:
- 引入乌尔都语专用预处理资源显著提高了乌尔都语语言处理 (ULP) 的效率.
- 开发的基于LSTM的模型显示了在乌尔都语社交媒体内容中准确检测威胁的巨大潜力.
- 这项研究为更强大的乌尔都语自然语言理解和分析提供了基础性一步.
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