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}
}
The paper deals with the identification of the dative/non-dative interpretation of a non-prepositional noun (the head noun in a prepositional group) in a rule-based automatic morphological disambiguation of Czech sentences. Based on the valency considerations including syntactic functions of the complements of verbs (these functions being an object or an adverbial) and resulting in the lists of verbs having / not having object dative valency, negative and positive non-heuristic (safe) as well as heuristic rules are presented. The negative rules assign a noun a non-dative interpretation using a list of verbs that cannot tolerate a dative noun in their close vicinity, whereas the positive ones specify a given noun as being in the dative case on the basis of the verbs having an object dative valency, an object being either the only object of the verb, or its second indirect object. The paper deals only with the non-prepositional nouns which are much more difficult to disambiguate than the prepositional ones.
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.
Corpus of contemporary written (printed) Czech sized 3.6 GW (i.e. 4.3 billion tokens). It covers mostly the period of 1990–2014 and it is a traditional corpus (as opposed to the web-crawled corpora) with rich metadata containing bibliographical information etc. Although it contains a wide range of text types (fiction, non-fiction, newspapers), the newspapers prevail noticeably. The corpus is lemmatized and morphologically annotated by a combination of stochastic and rule-based methods.
The corpus is provided in a (semi-XML) vertical format used as an input to the Manatee query engine. The data thus correspond to the corpus available via the KonText query interface to registered users of the CNC at http://www.korpus.cz with one important exception: the corpus are shuffled, i.e. divided into blocks sized max. 100 words (respecting the sentence boundaries) with ordering randomized within the given document.
Corpus of contemporary written (printed) Czech sized 4.7 GW (i.e. 5.7 billion tokens). It covers mostly the 1990-2019 period and features rich metadata including detailed bibliographical information, text-type classification etc. SYN v9 contains a wide variety of text types (fiction, non-fiction, newspapers), but the newspapers prevail noticeably. The corpus is lemmatized and morphologically tagged by the new CNC tagset first utilized for the annotation of the SYN2020 corpus.
SYN v9 is provided in a CoNLL-U-like vertical format used as an input to the Manatee query engine. The data thus correspond to the corpus available via the KonText query interface to the registered users of CNC at http://www.korpus.cz with one important exception: the corpus is shuffled, i.e. divided into blocks sized max. 100 words (respecting the sentence boundaries) with ordering randomized within the given document.
Balanced corpus of contemporary written Czech sized 100 MW. It was created as a representation of written language from 2000–2004 and thus it contains a wide range of text types and genres (fiction, professional literature, newspapers etc.) in balanced proportions. The corpus is lemmatized and morphologically tagged by a combination of stochastic and rule-based methods.
The corpus is provided in a (semi-XML) vertical format used as an input to the Manatee query engine. The data thus correspond to the corpus available via query interface to registered users of the CNC with one important exception: they are shuffled, i.e. divided into blocks sized max. 100 words (respecting the sentence boundaries) whose ordering was randomized within the given document. and MSM0021620823 – Český národní korpus a korpusy dalších jazyků