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}
}
Corpus of texts in 12 languages. For each language, we provide one training, one development and one testing set acquired from Wikipedia articles. Moreover, each language dataset contains (substantially larger) training set collected from (general) Web texts. All sets, except for Wikipedia and Web training sets that can contain similar sentences, are disjoint. Data are segmented into sentences which are further word tokenized.
All data in the corpus contain diacritics. To strip diacritics from them, use Python script diacritization_stripping.py contained within attached stripping_diacritics.zip. This script has two modes. We generally recommend using method called uninames, which for some languages behaves better.
The code for training recurrent neural-network based model for diacritics restoration is located at https://github.com/arahusky/diacritics_restoration.
Czech Contracts dataset was created as a part of the thesis Low-resource Text Classification (2021), A. Szabó, MFF UK.
Contracts are obtained from the Hlídač Státu web portal. Labels in the development and training set are automatically classified on the basis of the keyword method according to the thesis Automatická klasifikace smluv pro portál HlidacSmluv.cz, J. Maroušek (2020), MFF UK. For this reason, the goal in the classification is not to achieve 100% on the development set, as the classification contains a certain amount of noise. The test set is manually annotated. The dataset contains a total of 97493 contracts.
CERED (Czech Relationship Dataset) is a family of datasets created via distant supervision on Czech Wikipedia and Wikidata. It was created as part of a thesis on Relationship Extraction (2020).
CERED0 is the largest dataset, it lacks negative relation and its relation inventory is huge.
CERED*n* is a subset of CERED*n-1* that satisfies some conditions. The methodology of curating the datasets is detailed in the thesis.
The format of the data is jsonL and the tools used to generate the dataset is python.
The Czech translation of SQuAD 2.0 and SQuAD 1.1 datasets contains automatically translated texts, questions and answers from the training set and the development set of the respective datasets.
The test set is missing, because it is not publicly available.
The data is released under the CC BY-NC-SA 4.0 license.
If you use the dataset, please cite the following paper (the exact format was not available during the submission of the dataset): Kateřina Macková and Straka Milan: Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfer, presented at TSD 2020, Brno, Czech Republic, September 8-11 2020.
The dataset of handwritten Czech text lines, sourced from two chronicles (municipal chronicles 1931-1944, school chronicles 1913-1933).
The dataset comprises 25k lines machine-extracted from scanned pages, and provides manual annotation of text contents for a subset of size 2k.
A richly annotated and genre-diversified language resource, The Prague Dependency Treebank – Consolidated 1.0 (PDT-C 1.0, or PDT-C in short in the sequel) is a consolidated release of the existing PDT-corpora of Czech data, uniformly annotated using the standard PDT scheme. PDT-corpora included in PDT-C: Prague Dependency Treebank (the original PDT contents, written newspaper and journal texts from three genres); Czech part of Prague Czech-English Dependency Treebank (translated financial texts, from English), Prague Dependency Treebank of Spoken Czech (spoken data, including audio and transcripts and multiple speech reconstruction annotation); PDT-Faust (user-generated texts). The difference from the separately published original treebanks can be briefly described as follows: it is published in one package, to allow easier data handling for all the datasets; the data is enhanced with a manual linguistic annotation at the morphological layer and new version of morphological dictionary is enclosed; a common valency lexicon for all four original parts is enclosed. Documentation provides two browsing and editing desktop tools (TrEd and MEd) and the corpus is also available online for searching using PML-TQ.