On Modelling Corpus Citations in Computational Lexical Resources

Fahad Khan, Maxim Ionov, Christian Chiarcos, Laurent Romary, Gilles Sérasset, Besim Kabashi

Abstract
In this article we look at how two different standards for lexical resources, TEI and OntoLex, deal with corpus citations in lexicons. We will focus on how corpus citations in retrodigitised dictionaries can be modelled using each of the two standards since this provides us with a suitably challenging use case. After looking at the structure of an example entry from a legacy dictionary, we examine the two approaches offered by the two different standards by outlining an encoding for the example entry using both of them (note that this article features the first extended discussion of how the Frequency Attestation and Corpus (FrAC) module of OntoLex deals with citations). After comparing the two approaches and looking at the advantages and disadvantages of both, we argue for a combination of both. In the last part of the article we discuss different ways of doing this, giving our preference for a strategy which makes use of RDFa.
Anthology ID:
2024.lrec-main.1084
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
12385–12394
Language:
URL:
https://aclanthology.org/2024.lrec-main.1084
DOI:
Bibkey:
Cite (ACL):
Fahad Khan, Maxim Ionov, Christian Chiarcos, Laurent Romary, Gilles Sérasset, and Besim Kabashi. 2024. On Modelling Corpus Citations in Computational Lexical Resources. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12385–12394, Torino, Italia. ELRA and ICCL.
Cite (Informal):
On Modelling Corpus Citations in Computational Lexical Resources (Khan et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1084.pdf
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