Abstract
Anonymisation, that is identifying and neutralising sensitive references, is a crucial part of dataset creation. In this paper, we describe the anonymisation process of a Turkish-German code-switching corpus, namely SAGT, which consists of speech data and a treebank that is built on its transcripts. We employed a selective pseudonymisation approach where we manually identified sensitive references to anonymise and replaced them with surrogate values on the treebank side. In addition to maintaining data privacy, our primary concerns in surrogate selection were keeping the integrity of code-switching properties, morphosyntactic annotation layers, and semantics. After the treebank anonymisation, we anonymised the speech data by mapping between the treebank sentences and audio transcripts with the help of Praat scripts. The treebank is publicly available for research purposes and the audio files can be obtained via an individual licence agreement.- Anthology ID:
- 2022.lrec-1.595
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5557–5564
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.595
- DOI:
- Cite (ACL):
- Özlem Çetinoğlu and Antje Schweitzer. 2022. Anonymising the SAGT Speech Corpus and Treebank. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5557–5564, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Anonymising the SAGT Speech Corpus and Treebank (Çetinoğlu & Schweitzer, LREC 2022)
- PDF:
- https://preview.aclanthology.org/autopr/2022.lrec-1.595.pdf