Corpus AKCES 3 includes texts written in czech by non-native speakers (AKCES/CLAC - Czech Language Acquisition Corpora) and ESF (OPVK CZ.1.07/2.2.00/07.0259), MŠMT (MSM0021620825), UK (P10)
Corpus AKCES 4 includes texts written in czech by youth growing up in locations at risk of social exclusion (AKCES/CLAC - Czech Language Acquisition Corpora) and ESF (OPVK CZ.1.07/2.2.00/07.0259), MŠMT (MSM0021620825), UK (P10)
Essays written by non-native learners of Czech, a part of AKCES/CLAC – Czech Language Acquisition Corpora. CzeSL-SGT stands for Czech as a Second Language with Spelling, Grammar and Tags. Extends the “foreign” (ciz) part of AKCES 3 (CzeSL-plain) by texts collected in 2013. Original forms and automatic corrections are tagged, lemmatized and assigned erros labels. Most texts have metadata attributes (30 items) about the author and the text.
Essays written by non-native learners of Czech, a part of AKCES/CLAC – Czech Language Acquisition Corpora. CzeSL-SGT stands for Czech as a Second Language with Spelling, Grammar and Tags. Extends the “foreign” (ciz) part of AKCES 3 (CzeSL-plain) by texts collected in 2013. Original forms and automatic corrections are tagged, lemmatized and assigned erros labels. Most texts have metadata attributes (30 items) about the author and the text.
In addition to a few minor bugs, fixes a critical issue in Release 1: the native speakers of Ukrainian (s_L1:"uk") were wrongly labelled as speakers of "other European languages" (s_L1_group="IE"), instead of speakers of a Slavic language (s_L1_group="S"). The file is now a regular XML document, with all annotation represented as XML attributes.
AKCES-GEC is a grammar error correction corpus for Czech generated from a subset of AKCES. It contains train, dev and test files annotated in M2 format.
Note that in comparison to CZESL-GEC dataset, this dataset contains separated edits together with their type annotations in M2 format and also has two times more sentences.
If you use this dataset, please use following citation:
@article{naplava2019wnut,
title={Grammatical Error Correction in Low-Resource Scenarios},
author={N{\'a}plava, Jakub and Straka, Milan},
journal={arXiv preprint arXiv:1910.00353},
year={2019}
}
CzeSL-GEC is a corpus containing sentence pairs of original and corrected versions of Czech sentences collected from essays written by both non-native learners of Czech and Czech pupils with Romani background. To create this corpus, unreleased CzeSL-man corpus (http://utkl.ff.cuni.cz/learncorp/) was utilized. All sentences in the corpus are word tokenized.
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
This version fixes double annotation errors in train and dev M2 files, and also contains more metadata information.