组装浅或整合一个深? 面向一种轻量级的解决方案,用于识别字符的汉语文本分类
Jingrui Hou1, Ping Wang2,3
1Department of Computer Science, School of Science, Loughborough University, Loughborough, Leicestershire, United Kingdom.
PloS one
|July 28, 2023
概括
本研究介绍了一种轻量级组合方法 (LEGACT),用于识别字符的汉语文本分类. 它实现了与大型模型相匹配的性能,同时显著降低了计算成本.
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:
- 像中文这样的象形文字语言拥有独特的符号特征,可以增强语义表示.
- 现有的图形识别文本分类模型通常在计算上昂贵,限制了它们的实际应用.
- 需要有效的方法来平衡运行性能和计算成本,以识别汉字的汉语文本分类.
研究的目的:
- 开发一种轻量级且计算效率高的方法,用于识别汉字的汉语文本分类.
- 为了证明使用浅层网络的集体学习可以实现与大规模模型相比具有竞争力的结果.
- 为了突出符号特征在表示象形文字语言中的意义.
主要方法:
- 提出了一种轻量级的集体学习方法,用于识别字符的汉语文本分类 (LEGACT).
- 利用典型的浅层神经网络作为基础学习者.
- 雇佣机器学习分类器作为集体的元学习器.
主要成果:
- 该LEGACT方法实现了与大型变压器模型在字符识别中文文本分类中可比的性能.
- 通过整体方法证明了通过集成浅层神经网络的有效性.
- 提供了经验证据,证明了草图特征在象形文字语言表示中的重要性.
结论:
- 拟议的LEGACT方法提供了一种轻量级但强大的解决方案,用于识别汉字的汉语文本分类.
- 结合浅层神经网络的整体策略有效地减少了用于预测任务的计算工作负载.
- 象形文字的特征对于象形文字语言中强大的语义表示至关重要.
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