Integrating INCEpTION into larger annotation processes

Richard Eckart De Castilho, Jan-Christoph Klie, Iryna Gurevych


Abstract
Annotation tools are increasingly only steps in a larger process into which they need to be integrated, for instance by calling out to web services for labeling support or importing documents from external sources. This requires certain capabilities that annotation tools need to support in order to keep up. Here, we define the respective requirements and how popular annotation tools support them. As a demonstration for how these can be implemented, we adapted INCEpTION, a semantic annotation platform offering intelligent assistance and knowledge management. For instance, support for a range of APIs has been added to INCEpTION through which it can be controlled and which allow it to interact with external services such as authorization services, crowdsourcing platforms, terminology services or machine learning services. Additionally, we introduce new capabilities that allow custom rendering of XML documents and even the ability to add new JavaScript-based editor plugins, thereby making INCEpTION usable in an even wider range of annotation tasks.
Anthology ID:
2024.emnlp-demo.12
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Delia Irazu Hernandez Farias, Tom Hope, Manling Li
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–121
Language:
URL:
https://aclanthology.org/2024.emnlp-demo.12
DOI:
Bibkey:
Cite (ACL):
Richard Eckart De Castilho, Jan-Christoph Klie, and Iryna Gurevych. 2024. Integrating INCEpTION into larger annotation processes. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 110–121, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Integrating INCEpTION into larger annotation processes (Eckart De Castilho et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-demo.12.pdf