一个集成的集群和BERT框架,用于改进主题建模
1Department of Computer Science, Bharathidasan University, Tiruchirappalli, 620 023 Tamil Nadu India.
概括
本研究提出了一个统一的框架,结合了来自变压器的双向编码器表示 (BERT) 和隐藏的迪里克莱特分配 (LDA) 与聚类和维度降低,以改进主题建模. 这种方法提高了从大型文本数据集中提取的主题的连贯性和意义.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 主题建模对于从非结构化文本中提取见解至关重要.
- 隐性迪里克莱特分配 (LDA) 是一种常见但有时有限的主题建模技术.
- 集群算法提供有效的无监督信息提取.
研究的目的:
- 开发一个混合主题建模框架,整合BERT和LDA.
- 通过结合聚类和缩小维度来增强主题连贯性.
- 创建一个统一的方法,从大规模的文本公司中挖掘有意义的主题.
主要方法:
- 一种混合模型,结合了来自变压器的双向编码器表示 (BERT) 和隐藏的迪里克莱特分配 (LDA).
- 用于主题建模的集群算法.
- 缩小尺寸的技术 (PCA,t-SNE,UMAP) 来解决计算复杂性的问题.
- 在基准数据集上的实验验证.
主要成果:
- 使用BERT和LDA提出的基于集群的框架证明了它的有效性.
- 缩小尺寸有助于推断出更连贯的主题.
- 统一的方法成功地从大型文本集合中挖掘出有意义的主题.
结论:
- 集群和缩小维度的整合显著改善了BERT-LDA主题建模.
- 这个统一的框架为构建高级主题建模应用程序提供了强大的解决方案.
- 这种方法对于从大型文本体中提取连贯的主题是有效的.
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