让复杂主题模型更易懂，支持任意主题模型的交互式可视化。
topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation
- 不依赖特定模型，通用框架支持多种主题模型
- 通过交互式界面揭示文档、词与主题间的语义关系
- 突破传统词表局限，提供更全面的主题理解
主题模型是无需细读即可获取文本语料定性与定量洞察的统计工具，可应用于话语分析、预训练数据筛选、文本过滤等多种场景。由于主题模型参数丰富且结构复杂，用户常难以理解其输出。目前普遍做法是仅依据每个主题前10个高分词进行解读，这种方式提供的内容图景有限且存在偏差。精心设计的用户界面和可视化手段有助于用户更完整准确地理解主题模型结果。尽管已有部分可视化工具，但大多仅适用于特定类型的主题模型。本文提出topicwizard，一个面向模型无关的主题模型解释框架，提供直观、交互式的工具，帮助用户探究主题模型所学习到的文档、词语与主题之间的复杂语义关系。
原文摘要 · Abstract (English)
Topic models are statistical tools that allow their users to gain qualitative and quantitative insights into the contents of textual corpora without the need for close reading. They can be applied in a wide range of settings from discourse analysis, through pretraining data curation, to text filtering. Topic models are typically parameter-rich, complex models, and interpreting these parameters can be challenging for their users. It is typical practice for users to interpret topics based on the top 10 highest ranking terms on a given topic. This list-of-words approach, however, gives users a limited and biased picture of the content of topics. Thoughtful user interface design and visualizations can help users gain a more complete and accurate understanding of topic models' output. While some visualization utilities do exist for topic models, these are typically limited to a certain type of topic model. We introduce topicwizard, a framework for model-agnostic topic model interpretation, that provides intuitive and interactive tools that help users examine the complex semantic relations between documents, words and topics learned by topic models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。