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This paper introduces embComp, a novel approach for comparing two embeddings that capture the similarity between objects, such as word and document embeddings. We survey scenarios where comparing these embedding spaces is useful. From those scenarios, we derive common tasks, introduce visual analysis methods that support these tasks, and combine them into a comprehensive system. One of embComp's central features are overview visualizations that are based on metrics for measuring differences in the local structure around objects. Summarizing these local metrics over the embeddings provides global overviews of similarities and differences. Detail views allow comparison of the local structure around selected objects and relating this local information to the global views. Integrating and connecting all of these components, embComp supports a range of analysis workflows that help understand similarities and differences between embedding spaces. We assess our approach by applying it in several use cases, including understanding corpora differences via word vector embeddings, and understanding algorithmic differences in generating embeddings.
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Visual Exploration of Word Vector Embeddings
The use of word vector embeddings as the basis for many upstream tasks in text processing has lead to large improvements in accuracy. However, the exact reasons for this success largely remain unclear, as the properties and relations that these embeddings encode are often not well understood. Our goal in this ongoing project is to design effective interactive visualizations that help practitioners and researchers understand and compare such spaces better. The initial steps we have taken is to review relevant literature to identify properties and relations of word vectors that are important for various applications. From these, we derive basic tasks to inform the design of adequate and effective interactive visualizations that help users gain deeper insights into the structure of vector spaces. In addition, we present three initial designs to support these tasks.
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- Award ID(s):
- 1162037
- PAR ID:
- 10064420
- Date Published:
- Journal Name:
- IEEE Visualization Poster Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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