Abstract
The use of linked data within language-learning applications is an open research question. A research prototype is presented that applies linked-data principles to store linguistic annotation generated from language-learning content using a variety of NLP tools. The result is a database that links learning content, linguistic annotation and open-source resources, on top of which a diverse range of tools for language-learning applications can be built.- Anthology ID:
- W17-5005
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Joel Tetreault, Jill Burstein, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–51
- Language:
- URL:
- https://aclanthology.org/W17-5005
- DOI:
- 10.18653/v1/W17-5005
- Cite (ACL):
- Robyn Loughnane, Kate McCurdy, Peter Kolb, and Stefan Selent. 2017. Linked Data for Language-Learning Applications. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 44–51, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Linked Data for Language-Learning Applications (Loughnane et al., BEA 2017)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-5005.pdf