对于多标签文本分类的深度主动学习
Qunbo Wang1, Hangu Zhang2, Wentao Zhang2
1Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Scientific reports
|November 15, 2024
概括
这项研究介绍了BEAL,一种用于深度多标签文本分类的新型主动学习方法. BEAL使用贝叶斯深度学习和预期信心来高效地训练使用较少标记数据的模型.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 多标签文本分类 (MLTC) 将多个标签分配给文本.
- 深度学习模型在MLTC中显示出希望,但需要大量的标记数据.
- 标签多标签数据比单标签数据更为昂贵和耗时.
研究的目的:
- 开发深度MLTC模型的有效主动学习策略.
- 减少对大型标记数据集的依赖,以训练深度MLTC模型.
主要方法:
- 为深度MLTC提出BEAL (贝叶斯深度学习和预期信心).
- 使用贝叶斯深度学习来获得后置预测分布.
- 为不确定样本选择引入预期的基于信任的获取函数.
主要成果:
- 贝尔证明了深度MLTC的高效模型训练.
- 拟议的方法通过显著减少标记样本实现了模型融合.
- 在基准数据集上使用基于BERT的MLTC模型进行实验.
结论:
- BEAL有效地提高了深度MLTC模型训练的效率.
- 该方法减少了对广泛标记数据的需求,使MLTC更容易获得.
- BEAL代表了积极学习的重大进步,用于深度多标签分类.
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