融合式变压器-XL与双向循环网络用于网络欺凌检测
Md Mithun Hossain1, Md Shakil Hossain1, Md Shakhawat Hossain1
1Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
PeerJ. Computer science
|June 26, 2025
概括
这项研究介绍了一种新的混合模型,Fusion Transformer-XL,用于有效地检测孟加拉网络欺凌. 该模型实现了高精度和F1得分,证明了它对低资源语言的潜力.
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
背景情况:
- 在非英语语言中,由于语言细微差别和数据稀缺,网络欺凌的检测具有挑战性.
- 现有的方法经常与孟加拉语等低资源语言的复杂性作斗争.
研究的目的:
- 开发和评估一种强大的方法来识别在孟加拉文本数据中的网络欺凌.
- 解决当前网络欺凌检测系统在资源不足的语言环境中的局限性.
主要方法:
- 开发了一种混合型号,将变压器-XL与BiGRU-BiLSTM (融合变压器-XL) 结合起来.
- 进行了广泛的数据预处理,包括清理,增强和处理不平衡的类.
- 为了可解释性,使用预训练模型和局部可解释模型-不可知解释 (LIME) 的代币化.
主要成果:
- 融合变压器-XL型号实现了98.17%的高精度和98.18%的F1得分.
- 该模型与基线模型相比显示出更高的性能.
- 交叉数据集评估和k倍交叉验证证实了该模型的稳定性和适应性.
结论:
- 拟议的融合变压器-XL模型是有效的孟加拉网络欺凌检测,突出混合架构的力量.
- 这项研究有助于为资源有限的语言推进网络欺凌检测方法.
- 这项研究为在多语言NLP和社交媒体分析领域的进一步研究开辟了道路.
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