@inproceedings{kosakin-etal-2024-russian-learner,
title = "{R}ussian Learner Corpus: Towards Error-Cause Annotation for {L}2 {R}ussian",
author = "Kosakin, Daniil and
Obiedkov, Sergei and
Smirnov, Ivan and
Rakhilina, Ekaterina and
Vyrenkova, Anastasia and
Zalivina, Ekaterina",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1241",
pages = "14240--14258",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Russian Learner Corpus: Towards Error-Cause Annotation for L2 Russian
%A Kosakin, Daniil
%A Obiedkov, Sergei
%A Smirnov, Ivan
%A Rakhilina, Ekaterina
%A Vyrenkova, Anastasia
%A Zalivina, Ekaterina
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F kosakin-etal-2024-russian-learner
%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.
%U https://aclanthology.org/2024.lrec-main.1241
%P 14240-14258
Markdown (Informal)
[Russian Learner Corpus: Towards Error-Cause Annotation for L2 Russian](https://aclanthology.org/2024.lrec-main.1241) (Kosakin et al., LREC-COLING 2024)
ACL
- Daniil Kosakin, Sergei Obiedkov, Ivan Smirnov, Ekaterina Rakhilina, Anastasia Vyrenkova, and Ekaterina Zalivina. 2024. Russian Learner Corpus: Towards Error-Cause Annotation for L2 Russian. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14240–14258, Torino, Italia. ELRA and ICCL.