基于预训练模型和自动编码器的文本聚类
Qiang Xu1, Hao Gu1, ShengWei Ji1
1School of Artificial Intelligence and Big Data, Hefei University, Hefei, Anhui, China.
Frontiers in computational neuroscience
|January 22, 2024
概括
这项研究引入了一种用于医学文本集群的新型深度学习方法,通过结合预先训练的语言模型和自动编码器来提高准确性. 这种方法增强了医疗决策和数据管理.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 文本聚类对于医疗决策,患者记录管理和信息检索至关重要.
- 传统的方法,如词袋,在大型医疗数据集中与高维度,稀疏性和上下文作斗争.
- 深度学习为复杂的非线性数据提供先进的解决方案,优于传统的集群模型.
研究的目的:
- 为医疗数据开发一个先进的文本聚类模型.
- 解决传统集群算法在处理复杂医疗文本方面的局限性.
- 提高医疗数据分析和检索的准确性和效率.
主要方法:
- 该研究将预训练语言模型 (PLM) 与深层嵌入集群 (DEC) 模型集成在一起.
- PLM捕获顺序的文本信息,包括单词位置和上下文.
- 在DEC内部的自动编码器学习数据表示和信息集群,减少噪音.
主要成果:
- 与现有的文本集群算法相比,拟议的模型在四个公共数据集上表现出优异的性能.
- PLM和DEC的集成有效地处理高维和复杂的医疗文本数据.
- 该模型显示了在医疗数据集群中实际应用的巨大潜力.
结论:
- 将预先训练的语言模型与深层嵌入集群结合起来,为医学文本分析提供了一种强大的方法.
- 这种方法克服了医疗保健领域传统文本聚类技术的局限性.
- 开发的模型为增强医疗数据管理和决策支持系统提供了强大的解决方案.
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