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利用深度学习来检测书面语言中的有毒评论.

Muhammad Shahid1, Muhammad Umair1, Muhammad Amjad Iqbal1

  • 1Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan.

PeerJ. Computer science
|February 3, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型有效地检测乌尔都语的有毒评论,优于现有的方法. 这一进步对于改善低资源语言的在线内容调节至关重要.

关键词:
分类 分类 分类 分类.这是一本"Corpus Corpus".写字的语言 写字的语言深度学习是一种深度学习.有毒的评论 有毒的评论

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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:

  • 社交媒体促进了公众的表达,包括仇恨言论和有害内容.
  • 检测有毒的在线内容对于社区安全和福祉至关重要.
  • 乌尔都语是一个广泛使用的低资源语言,缺乏用于检测有毒评论的标准工具.

研究的目的:

  • 开发和评估一种用于识别乌尔都文中有毒评论的新型模型.
  • 为了应对标记化和分类复杂,往往不语法性的乌尔都语评论的挑战.
  • 为各种语言平台增强内容调节能力.

主要方法:

  • 一个新的深度学习模型,利用变换器来识别乌尔都语句子中的突出特征.
  • 文字的二进制分类来标记有毒评论.
  • 评估模型,包括来自变压器 (BERT) 和GPT-2的双向编码器表示.

主要成果:

  • 拟议的微调模型实现了88.38%的精度,超过了现有方法.
  • 来自变压器 (BERT) 的双向编码器表示显示出强大的性能,准确率为85.45%,精度为85.71%.
  • 在这项研究中,GPT-2被确定为表现最低的模型.

结论:

  • 开发的模型代表了乌尔都语有毒评论检测的重大进步.
  • 这些发现有助于优化在各种语言和平台上的内容调节.
  • 这项研究解决了在低资源语言环境中对有效工具的关键需求.