IEEE VIS 2024 Content: CataAnno: An Ancient Catalog Annotator for Annotation Cleaning by Recommendation

Honorable Mention

CataAnno: An Ancient Catalog Annotator for Annotation Cleaning by Recommendation

Hanning Shao - Peking University, Beijing, China

Xiaoru Yuan - Peking University, Beijing, China

Room: Bayshore V

2024-10-16T13:30:00Z GMT-0600 Change your timezone on the schedule page
2024-10-16T13:30:00Z
Exemplar figure, described by caption below
Classical bibliography examines the books throughout history and reveal cultural development by researching preserved catalogs. Through interdisciplinary collaboration, we propose CataAnno, an intelligent annotation system that helps with annotation cleaning of these ancient catalogs. Learning base recommendations and convenient interactions supported by CataAnno enhances the consistency and efficiency of the annotation process.
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Keywords

Digital humanities, text annotation tool, text visualization, machine learning, catalog

Abstract

Classical bibliography, by researching preserved catalogs from both official archives and personal collections of accumulated books, examines the books throughout history, thereby revealing cultural development across historical periods. In this work, we collaborate with domain experts to accomplish the task of data annotation concerning Chinese ancient catalogs. We introduce the CataAnno system that facilitates users in completing annotations more efficiently through cross-linked views, recommendation methods and convenient annotation interactions. The recommendation method can learn the background knowledge and annotation patterns that experts subconsciously integrate into the data during prior annotation processes. CataAnno searches for the most relevant examples previously annotated and recommends to the user. Meanwhile, the cross-linked views assist users in comprehending the correlations between entries and offer explanations for these recommendations. Evaluation and expert feedback confirm that the CataAnno system, by offering high-quality recommendations and visualizing the relationships between entries, can mitigate the necessity for specialized knowledge during the annotation process. This results in enhanced accuracy and consistency in annotations, thereby enhancing the overall efficiency.