Abstract
Data annotation is a critical but the most expensive step in any text analytics project. There have been several frameworks built for enabling and easing this step. Most of these frameworks are however either not easy to be configured to specific users’ needs, have no functionalities for annotating text pairs, or lack of efficient mechanism for data management and progress monitoring. Moreover, they have mostly no graphical user interfaces that are specifically designed for mobile devices. In this paper, we introduce Hannotate, a highly flexible, lightweight web-based framework that provides functionalities for a wide range of text annotation from both desktop and mobile devices. Our framework inherits the advantages of the typical existing ones while allowing users to easily customize the annotation work according to their demand and budget. The framework also supports users in managing data, monitoring the progress, and giving feedback to annotators.
H. Dao—Independent Researcher. Email: hoangdhph04904@gmail.com.
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Acknowledgement
This work is supported by Vingroup Innovation Foundation (VINIF) in project code VINIF.2020.DA14
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To, TT., Dao, H., Nguyen, H., Do, TH., Hoang, TA. (2023). Hannotate: Flexible Annotation for Text Analytics from Anywhere. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_24
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DOI: https://doi.org/10.1007/978-3-031-43458-7_24
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