语义概念空间：基于词嵌入投影的主题模型优化
Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections
摘要：
我们提出了一个框架，允许用户将他们的领域知识的语义主题模型细化，同时保持模型不可知。我们的方法使用户能够（1）理解模型的语义空间，（2）识别潜在的冲突和问题的区域，（3）根据他们的理解重新调整概念的语义关系，直接影响主题建模。这些任务由交互式可视化分析工作区支持，该工作区使用词嵌入投影来定义概念区域，然后可以对其进行细化。用户细化的概念是独立于特定的文档集合，并可以转移到相关的语料库。概念空间中的所有用户交互直接影响底层向量空间模型的语义关系，这反过来又改变了主题建模。除了直接操作之外，我们的系统还通过推荐的交互来指导用户的决策过程，这些交互指出了潜在的改进。这种有针对性的改进旨在最大限度地减少有效的人在回路过程所需的反馈。我们确认了通过我们的方法在两个用户研究中实现的改进，通过我们的视觉知识外化和学习过程显示主题模型质量的提高。
英文摘要：
We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic. Our approach enables users to (1) understand the semantic space of the model, (2) identify regions of potential conflicts and problems, and (3) readjust the semantic relation of concepts based on their understanding, directly influencing the topic modeling. These tasks are supported by an interactive visual analytics workspace that uses word-embedding projections to define concept regions which can then be refined. The user-refined concepts are independent of a particular document collection and can be transferred to related corpora. All user interactions within the concept space directly affect the semantic relations of the underlying vector space model, which, in turn, change the topic modeling. In addition to direct manipulation, our system guides the users' decision-making process through recommended interactions that point out potential improvements. This targeted refinement aims at minimizing the feedback required for an efficient human-in-the-loop process. We confirm the improvements achieved through our approach in two user studies that show topic model quality improvements through our visual knowledge externalization and learning process.
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。
