Linked Data for Language-Learning Applications

Robyn Loughnane, Kate McCurdy, Peter Kolb, Stefan Selent

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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/W17-5005.pdf
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