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}
}
Automatically generated spelling correction corpus for Czech (Czesl-SEC-AG) is a corpus containg text with automatically generated spelling errors. To create spelling errors, a character error model containing probabilities of character substitution, insertion, deletion and probabilities of swaping two adjacent characters is used. Besides these probabilities, also the probabilities of changing character casing are considered. The original clean text on which the spelling errors were generated is PDT3.0 (http://hdl.handle.net/11858/00-097C-0000-0023-1AAF-3). The original train/dev/test sentence split of PDT3.0 corpus is preserved in this dataset.
Besides the data with artificial spelling errors, we also publish texts from which the character error model was created. These are the original manual transcript of an audiobook Švejk and its corrected version performed by authors of Korektor (http://ufal.mff.cuni.cz/korektor). These data are similarly to CzeSL Grammatical Error Correction Dataset (CzeSL-GEC: http://hdl.handle.net/11234/1-2143) processed into four sets based on error difficulty present.
CoNLL 2017 and 2018 shared tasks:
Multilingual Parsing from Raw Text to Universal Dependencies
This package contains the test data in the form in which they ware presented
to the participating systems: raw text files and files preprocessed by UDPipe.
The metadata.json files contain lists of files to process and to output;
README files in the respective folders describe the syntax of metadata.json.
For full training, development and gold standard test data, see
Universal Dependencies 2.0 (CoNLL 2017)
Universal Dependencies 2.2 (CoNLL 2018)
See the download links at http://universaldependencies.org/.
For more information on the shared tasks, see
http://universaldependencies.org/conll17/
http://universaldependencies.org/conll18/
Contents:
conll17-ud-test-2017-05-09 ... CoNLL 2017 test data
conll18-ud-test-2018-05-06 ... CoNLL 2018 test data
conll18-ud-test-2018-05-06-for-conll17 ... CoNLL 2018 test data with metadata
and filenames modified so that it is digestible by the 2017 systems.
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.
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.
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.
This entry contains the SumeCzech dataset and the metric RougeRAW used for evaluation. Both the dataset and the metric are described in the paper "SumeCzech: Large Czech News-Based Summarization Dataset" by Milan Straka et al.
The dataset is distributed as a set of Python scripts which download the raw HTML pages from CommonCrawl and then process them into the required format.
The MPL 2.0 license applies to the scripts downloading the dataset and to the RougeRAW implementation.
Note: sumeczech-1.0-update-230225.zip is the updated release of the SumeCzech download script, including the original RougeRAW evaluation metric. The download script was modified to use the updated CommonCraw download URL and to support Python 3.10 and Python 3.11. However, the downloaded dataset is still exactly the same. The original archive sumeczech-1.0.zip was renamed to sumeczech-1.0-obsolete-180213.zip and is kept for reference.
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