临床文本分类中的注意力机制:比较评估
IEEE journal of biomedical and health informatics
|January 19, 2024
概括
标签明智的注意力机制提高神经网络在生物医学文本分类中的性能. 使用预训练的参考信息和层次设计提高了MIMIC III数据集上的分类准确性.
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.
- 生物医学信息学 生物医学信息学
背景情况:
- 注意力机制对于生物医学文本分类中的神经网络至关重要.
- 标签明智的注意力机制通过使用标签特定信息来增强模型歧视.
- 当前的方法任意选择参考信息,影响模型性能.
研究的目的:
- 用隐式或明确的参考信息初写的标签明智的注意力来评估.
- 在各种文本编码架构中比较标签明智的注意力与基线方法.
- 引入一个扩展的标签明智的注意,包括代码层次信息.
主要方法:
- 在MIMIC III数据集上进行了实验.
- 评估标签明智的注意力,含有隐式/明确的参考信息,与目标注意力和文本编码器特定方法相比.
- 在四个文本编码架构中进行测试:CNN,两个RNN和Transformer.
- 引入了一个分层的标签明智的注意力扩展.
主要成果:
- 预先训练的参考信息和层次设计显著改善了分类性能.
- 在较大的数据集和标签空间上,性能增长不那么明显.
- 用注意力能量得分来分析性能和可解释性差异.
结论:
- 通过预先训练的参考信息和层次结构来初始化标签的注意力,可以增强生物医学文本的分类.
- 这些增强的有效性因数据集大小和标签空间复杂性而异.
- 注意力机制分析为跨不同架构的模型行为提供了见解.
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