主题模型的大规模评估和2D文本空间化尺寸缩小方法
IEEE transactions on visualization and computer graphics
|October 23, 2023
概括
可解释的主题模型和t-SNE缩小维度可以创建高质量的文本空间化. 本基准评估提供了使用主题建模进行有效文本可视化设计的指导方针.
科学领域:
- 计算语言学计算语言学
- 数据可视化数据可视化
- 机器学习是机器学习.
背景情况:
- 主题模型揭示了文本体中的语义结构.
- 主题模型与缩小维度相结合,可以创建文档空间化.
- 对于高质量的文本空间化的最佳组合是未知的.
研究的目的:
- 评估主题模型和缩小文本空间化的维度.
- 确定有效的组合,以获得准确和感知有效的布局.
- 为设计文本可视化提供准则.
主要方法:
- 使用基准数据集进行大规模计算评估.
- 测试了主题模型和缩小维度算法的组合.
- 使用局部/全球属性和感知有效性的指标量化布局质量.
主要成果:
- 产生了超过45,000个独特的布局和质量指标.
- 可解释的主题模型显著改善了体结构的捕获.
- 推使用t-分布式随机邻居嵌入 (t-SNE) 进行维度缩小.
结论:
- 提出了有效的文本空间化设计的指导方针.
- 可解释的主题模型对于语义结构表示至关重要.
- t-SNE 是一个非常有效的缩小维度的技术来完成这个任务.
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