日本文学作品中作者归因的BERT和基于特征的模型的综合组合
Taisei Kanda1, Mingzhe Jin2,3, Wataru Zaitsu4
1Graduate School of Culture and Information Science, Doshisha University, Kyoto, Japan.
Frontiers in artificial intelligence
|October 8, 2025
概括
这项研究结合了BERT和传统的作者归因方法,大大提高了小样本任务的准确性. 综合方法的表现优于单个模型,强调了模型多样性的重要性.
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
- 机器学习 机器学习
背景情况:
- 传统的作者归因 (AA) 依赖于统计和风格特征.
- 像BERT这样的预先训练的语言模型显示出希望,但在小样本AA中未得到充分探索.
- 将BERT与传统方法相结合是一个关键的挑战.
研究的目的:
- 通过结合传统和基于BERT的方法来提高小样本的作者归因性能.
- 将BERT和传统分类器的准确性进行比较.
- 在集合方法中评估模型特征的相互作用.
主要方法:
- 开发了一种结合BERT和基于特征的分类器的全新集成组合方法.
- 与传统组合技术进行基准测试.
- 使用两个文学集体验证,每个集体有10个作者,包含5个BERT变体,3个特征类型和2个分类器架构.
主要成果:
- 在小样本AA中,BERT的表现优于传统的基于特征的方法.
- 组合模型 (基于BERT,基于功能和集成) 超过了独立模型.
- 综合组合获得了最高的F1得分 (0.96在Corpus B上),明显优于单个模型 (p < 0.012).
结论:
- 综合合奏方法对于小样本的作者归因非常有效.
- 预培训数据和模型多样性对于优化组合学习中的语言模型至关重要.
- 结果为AA研究和人工通用智能开发提供了洞察力.
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