关于文本空间化的潜在嵌入与降维的大规模敏感性分析
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
AI总结：
本文提出了一项关于文本空间化中潜在嵌入与降维的大规模敏感性研究，通过两阶段方法分析布局在语料库、超参数及初始化变化下的稳定性，为布局算法选择提供了指导。
摘要：
文本语料库中文档之间的语义相似性可以通过基于二维散点图布局的类地图隐喻进行可视化。这些布局是通过对文档-词项矩阵进行降维或在潜在嵌入（包括 topic models）中的表示得到的。因此，生成的布局取决于输入数据和降维的超参数，并受其变化的影响。此外，生成的布局还受输入数据和降维超参数变化的影响。然而，布局的此类变化需要用户付出额外的认知努力。在这项工作中，我们提出了一项敏感性研究，分析这些布局在以下方面的稳定性：（1）文本语料库的变化，（2）超参数的变化，以及（3）初始化的随机性。我们的方法分为两个阶段：数据测量和数据分析。首先，我们针对三个文本语料库和六种文本嵌入的组合，以及受网格搜索启发的降维超参数选择，推导出了布局。随后，我们通过十个指标量化了布局的相似性，涉及局部和全局结构以及类别分离。其次，我们在描述性统计分析中分析了由此产生的 42817 个表格数据点。由此，我们为布局算法的知情决策推导了指南，并突出了特定的超参数设置。我们在 https://github.com/hpicgs/Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study 提供了实现代码作为 Git 仓库，并在 https://doi.org/10.5281/zenodo.12772898 提供了结果作为 Zenodo 存档。
英文摘要：
The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on two-dimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at https://github.com/hpicgs/Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study and results as Zenodo archive at https://doi.org/10.5281/zenodo.12772898.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
