%0 Conference Paper %1 kosakin-etal-2024-russian %A Kosakin, Daniil %A Obiedkov, Sergei %A Smirnov, Ivan %A Rakhilina, Ekaterina %A Vyrenkova, Anastasia %A Zalivina, Ekaterina %B Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) %C Torino, Italia %D 2024 %E Calzolari, Nicoletta %E Kan, Min-Yen %E Hoste, Veronique %E Lenci, Alessandro %E Sakti, Sakriani %E Xue, Nianwen %I ELRA and ICCL %K imported xack %P 14240--14258 %T Russian Learner Corpus: Towards Error-Cause Annotation for L2 Russian %U https://aclanthology.org/2024.lrec-main.1241 %X Russian Learner Corpus (RLC) is a large collection of learner texts in Russian written by native speakers of over forty languages. Learner errors in part of the corpus are manually corrected and annotated. Diverging from conventional error classifications, which typically focus on isolated lexical and grammatical features, the RLC error classification intends to highlight learners' strategies employed in the process of text production, such as derivational patterns and syntactic relations (including agreement and government). In this paper, we present two open datasets derived from RLC: a manually annotated full-text dataset and a dataset with crowdsourced corrections for individual sentences. In addition, we introduce an automatic error annotation tool that, given an original sentence and its correction, locates and labels errors according to a simplified version of the RLC error-type system. We evaluate the performance of the tool on manually annotated data from RLC.

@inproceedings{kosakin-etal-2024-russian, abstract = {Russian Learner Corpus (RLC) is a large collection of learner texts in Russian written by native speakers of over forty languages. Learner errors in part of the corpus are manually corrected and annotated. Diverging from conventional error classifications, which typically focus on isolated lexical and grammatical features, the RLC error classification intends to highlight learners{'} strategies employed in the process of text production, such as derivational patterns and syntactic relations (including agreement and government). In this paper, we present two open datasets derived from RLC: a manually annotated full-text dataset and a dataset with crowdsourced corrections for individual sentences. In addition, we introduce an automatic error annotation tool that, given an original sentence and its correction, locates and labels errors according to a simplified version of the RLC error-type system. We evaluate the performance of the tool on manually annotated data from RLC.}, added-at = {2024-12-16T11:30:03.000+0100}, address = {Torino, Italia}, author = {Kosakin, Daniil and Obiedkov, Sergei and Smirnov, Ivan and Rakhilina, Ekaterina and Vyrenkova, Anastasia and Zalivina, Ekaterina}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/29e34844957a88d9fd9fc46d367fb76fd/scadsfct}, booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}, interhash = {c79829e9662358d7a3fb07d6a0288973}, intrahash = {9e34844957a88d9fd9fc46d367fb76fd}, keywords = {imported xack}, month = may, pages = {14240--14258}, publisher = {ELRA and ICCL}, timestamp = {2025-07-29T10:29:54.000+0200}, title = {{R}ussian Learner Corpus: Towards Error-Cause Annotation for {L}2 {R}ussian}, url = {https://aclanthology.org/2024.lrec-main.1241}, year = 2024 }