一个可解释的多变压器组合,用于基于文本的电影类型分类
Faheem Shaukat1, Naveed Ejaz2, Zeeshan Ashraf3
1Department of Computing and Technology, IQRA University, Islamabad, Pakistan.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了一个集体深度学习模型,使用电影情节进行多标签的类型分类. 该模型实现了最先进的结果,通过利用文本数据和可解释性技术,优于现有方法.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
背景情况:
- 多标签电影的类型分类是复杂的,因为类型的重叠.
- 现有的方法主要依赖于视听数据,不充分利用文本.
- 基于文本的方法为准确的类型预测提供了尚未开发的潜力.
研究的目的:
- 开发一个集体深度学习模型,用于使用电影情节进行多标签电影类型分类.
- 探索基于文本的模式在类型预测中的有效性.
- 使用局部可解释模型-不可知解释 (LIME) 增强模型的解释性.
主要方法:
- 文字电影情节的预处理.
- 使用三种基于变压器的模型:BERT,DistilBERT和RoBERTa.
- 通过加权软投票组合方法结合预测.
- 应用LIME用于模型可解释性.
主要成果:
- 在Trailers12K和LMTD9数据集上实现了最先进的性能.
- 达到微平均精度分别为80.10%和80.37%.
- 显著优于传统和先进的深度学习模型.
- 证明了结合各种变压器模型的有效性.
结论:
- 文本数据,特别是电影情节,对于自动化的多标签类型分类非常有效.
- 集团深度学习模型从文本中捕获细微的类型信息.
- 像LIME这样的可解释性方法对于理解类型分类模型至关重要.
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