Abstract
We introduce a pilot annotation of Russian learner data with syntactic dependency relations. The annotation is performed on a subset of sentences from RULEC-GEC and RU-Lang8, two error-corrected Russian learner datasets. We provide manually labeled Universal Dependency (UD) trees for 500 sentence pairs, annotating both the original (source) and the corrected (target) version of each sentence. Further, we outline guidelines for annotating learner Russian data containing non-standard erroneous text and analyze the effect that the individual errors have on the resulting dependency trees. This study should contribute to a wide range of computational and theoretical research directions in second language learning and grammatical error correction.- Anthology ID:
- 2024.lrec-main.1486
- 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:
- 17112–17119
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1486
- DOI:
- Cite (ACL):
- Alla Rozovskaya. 2024. Universal Dependencies for Learner Russian. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17112–17119, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Universal Dependencies for Learner Russian (Rozovskaya, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1486.pdf