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Updated: Jun 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

罗马乌尔都语仇恨言论检测使用混合机器学习模型和超参数优化.

Waqar Ashiq¹, Samra Kanwal², Adnan Rafique³

¹Department of Software Engineering, University of Management and Technology, Lahore, 54590, Pakistan.

Scientific reports

|November 20, 2024

概括

此摘要是机器生成的。

这项研究介绍了一种新的混合模型,用于检测社交媒体上的罗马乌尔都文本中的仇恨言论. 该模型将深度学习和变压器功能与机器学习分类器相结合,在基准数据集上获得最先进的结果.

关键词:

深度学习是一种深度学习.仇恨言论的检测仇恨言论的检测模型优化模型优化乌尔都语文文本分类的分类

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Last Updated: Jun 7, 2025

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科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
人工智能 (AI) 是一种人工智能.
社交媒体分析社交媒体分析

背景情况:

社交媒体的增长导致网络欺凌和仇恨言论的增加.
自动检测仇恨言论 (HSD) 是一个关键的NLP研究领域.
关于乌尔都语HSD的研究是有限的,特别是在罗马乌尔都文本方面.

研究的目的:

开发和评估一个新的混合模型,用于罗曼乌尔都语在Twitter上检测仇恨言论.
解决乌尔都语研究的短缺问题HSD.
探索深度学习,变压器模型和机器学习算法的集成.

主要方法:

开发了一种混合模型,将深度学习 (DL) 和变压器模型集成为特征提取.
使用机器学习算法 (MLA) 来进行分类.
使用超参数优化 (HPO) 技术,如网格搜索,随机搜索和贝叶斯优化.
在两个公开的罗马乌尔都语体上评估模型:HS-RU-20和RUHSOLD.

主要成果:

使用多语言BERT (MBERT) 与支持矢量机 (SVM) 分类器的混合模型,通过随机搜索 (RS) 进行优化,实现了最先进的性能.
在HS-RU-20体上,实现了0.93准确度和0.95F1得分 (中立-敌对) 和0.89准确度和0.88F1得分 (仇恨言论-攻击).
在RUHSOLD体上,实现了0.95准确度和0.94F1得分 (粗粒度) 和0.87准确度和0.84F1得分 (细粒度).

结论:

拟议的混合方法在罗马乌尔都语仇恨言论检测方面表现出显著的有效性.
集成先进的NLP技术和优化的机器学习分类器对于高性能至关重要.
这项研究为打击在线仇恨言论在乌尔都语语境中的重要工具做出了贡献.