Abstract
Corpus data is the main source of data for natural language processing applications, however no standard or model for corpus data has become predominant in the field. Linguistic linked data aims to provide methods by which data can be made findable, accessible, interoperable and reusable (FAIR). However, current attempts to create a linked data format for corpora have been unsuccessful due to the verbose and specialised formats that they use. In this work, we present the Teanga data model, which uses a layered annotation model to capture all NLP-relevant annotations. We present the YAML serializations of the model, which is concise and uses a widely-deployed format, and we describe how this can be interpreted as RDF. Finally, we demonstrate three examples of the use of the Teanga data model for syntactic annotation, literary analysis and multilingual corpora.- Anthology ID:
- 2024.ldl-1.9
- Volume:
- Proceedings of the 9th Workshop on Linked Data in Linguistics @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Christian Chiarcos, Katerina Gkirtzou, Maxim Ionov, Fahad Khan, John P. McCrae, Elena Montiel Ponsoda, Patricia Martín Chozas
- Venues:
- LDL | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 66–74
- Language:
- URL:
- https://aclanthology.org/2024.ldl-1.9
- DOI:
- Cite (ACL):
- John P. McCrae, Priya Rani, Adrian Doyle, and Bernardo Stearns. 2024. Teanga Data Model for Linked Corpora. In Proceedings of the 9th Workshop on Linked Data in Linguistics @ LREC-COLING 2024, pages 66–74, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Teanga Data Model for Linked Corpora (McCrae et al., LDL-WS 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.ldl-1.9.pdf