HUMAN: Hierarchical Universal Modular ANnotator

Moritz Jonathan Wolf, Dana Ruiter, Ashwin Geet D’Sa, Liane Reiners, Jan Alexandersson, Dietrich Klakow

In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Conference on Empirical Methods in Natural Language Processing (EMNLP-2020) November 16-20 Online-Conference Seiten 55-61 Association for Computational Linguistics 2020.


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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2020.emnlp-demos.8_(1).pdf (pdf, 2 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence