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多语言希望使用转移学习模型从推特中检测语音.

Muhammad Ahmad1, Iqra Ameer2, Wareesa Sharif3

  • 1Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-PN), 07738, Mexico City, Mexico.

Scientific reports
|March 16, 2025
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概括
此摘要是机器生成的。

研究人员开发了一种新方法来检测乌尔都语和英语的希望言论 (积极的在线内容). 使用基于翻译的方法和伯特变压器模型,他们实现了高精度,比基线模型提高了检测率.

关键词:
以及推特分析.贝尔特·贝尔特·贝尔特是什么意思深度学习是一种深度学习.希望的演讲希望的演讲机器学习是机器学习.罗伯塔·罗伯塔 (Roberta) 是一个女人.在SVM中,SVM是SVM.社交媒体 社交媒体转移学习转移学习

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科学领域:

  • 计算语言学 计算语言学
  • 自然语言处理自然语言处理.
  • 社交媒体分析 社交媒体分析

背景情况:

  • 社交媒体显著影响公共话语和社区情绪状态.
  • 仇恨言论是普遍存在的,但"希望言论"的支持和鼓励的在线内容也是如此.
  • 在几个语言中已经探索了希望语的自动检测,但不是乌尔都语和英语使用基于翻译的方法.

研究的目的:

  • 为了弥补乌尔都语和英语语言的差距,希望能够进行语音检测.
  • 创建一个新的英语和乌尔都语希望演讲的多语言数据集.
  • 使用最先进的机器学习,深度学习和转移学习模型对数据集进行基准测试.

主要方法:

  • 在英语和乌尔都语中开发一个多语言数据集.
  • 应用基于翻译的方法来应对多语言挑战.
  • 利用最先进的机器学习,深度学习和转移学习方法,包括Bert变压器模型,进行基准测试.

主要成果:

  • 伯特变压器模型实现了基准性能,英语准确率为87%,乌尔都语准确率为79%.
  • 这代表了英语的8.75%和乌尔都语的1.87%的改进,而不是基线支持矢量机 (SVM) 模型.
  • 严格的注释者选择和详细的指导方针显著提高了数据集质量.

结论:

  • 提出的基于翻译的方法,使用伯特变压器模型,对多语言希望语音检测是有效的.
  • 创建的英语-乌尔都语数据集和基准测试为该领域的未来研究提供了基础.
  • 高质量的注释过程对于开发用于社交媒体内容分析的强大数据集至关重要.