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HUMAN: Hierarchical Universal Modular ANnotator

Abstract : A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes. We introduce HUMAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.
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Conference papers
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Contributor : Ashwin Geet D'Sa Connect in order to contact the contributor
Submitted on : Tuesday, October 6, 2020 - 11:49:50 AM
Last modification on : Friday, May 20, 2022 - 3:11:37 AM

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  • HAL Id : hal-02958831, version 1
  • ARXIV : 2010.01080


Moritz Wolf, Dana Ruiter, Ashwin Geet d'Sa, Liane Reiners, Jan Alexandersson, et al.. HUMAN: Hierarchical Universal Modular ANnotator. EMNLP 2020 System Demonstration, Nov 2020, Punta Cana (Virtual), Dominican Republic. ⟨hal-02958831⟩



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