KorAP Architecture ― Diving in the Deep Sea of Corpus Data
Nils Diewald, Michael Hanl, Eliza Margaretha, Joachim Bingel, Marc Kupietz, Piotr Bański, Andreas Witt
Abstract
KorAP is a corpus search and analysis platform, developed at the Institute for the German Language (IDS). It supports very large corpora with multiple annotation layers, multiple query languages, and complex licensing scenarios. KorAP’s design aims to be scalable, flexible, and sustainable to serve the German Reference Corpus DeReKo for at least the next decade. To meet these requirements, we have adopted a highly modular microservice-based architecture. This paper outlines our approach: An architecture consisting of small components that are easy to extend, replace, and maintain. The components include a search backend, a user and corpus license management system, and a web-based user frontend. We also describe a general corpus query protocol used by all microservices for internal communications. KorAP is open source, licensed under BSD-2, and available on GitHub.- Anthology ID:
- L16-1569
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 3586–3591
- Language:
- URL:
- https://aclanthology.org/L16-1569
- DOI:
- Cite (ACL):
- Nils Diewald, Michael Hanl, Eliza Margaretha, Joachim Bingel, Marc Kupietz, Piotr Bański, and Andreas Witt. 2016. KorAP Architecture ― Diving in the Deep Sea of Corpus Data. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3586–3591, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- KorAP Architecture ― Diving in the Deep Sea of Corpus Data (Diewald et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/L16-1569.pdf