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}
}
Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/).
For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive.
Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions.
Update 2018-09-03
===============
Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.
The `corpipe23-corefud1.1-231206` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 (https://github.com/ufal/crac2023-corpipe). It is released under the CC BY-NC-SA 4.0 license.
The model is language agnostic (no _corpus id_ on input), so it can be used to predict coreference in any `mT5` language (for zero-shot evaluation, see the paper). However, note that the empty nodes must be present already on input, they are not predicted (the same settings as in the CRAC23 shared task).
The `corpipe23-corefud1.2-240906` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 <https://github.com/ufal/crac2023-corpipe>. It is released under the CC BY-NC-SA 4.0 license.
The model is language agnostic (no corpus id on input), so it can be in theory used to predict coreference in any `mT5` language. However, the model expects empty nodes to be already present on input, predicted by the https://www.kaggle.com/models/ufal-mff/crac2024_zero_nodes_baseline/.
This model was present in the CorPipe 24 paper as an alternative to a single-stage approach, where the empty nodes are predicted joinly with coreference resolution (via http://hdl.handle.net/11234/1-5672), an approach circa twice as fast but of slightly worse quality.
The `corpipe24-corefud1.2-240906` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 24 (https://github.com/ufal/crac2024-corpipe). It is released under the CC BY-NC-SA 4.0 license.
The model is language agnostic (no corpus id on input), so it can be in theory used to predict coreference in any `mT5` language.
This model jointly predicts also the empty nodes needed for zero coreference. The paper introducing this model also presents an alternative two-stage approach first predicting empty nodes (via https://www.kaggle.com/models/ufal-mff/crac2024_zero_nodes_baseline/) and then performing coreference resolution (via http://hdl.handle.net/11234/1-5673), which is circa twice as slow but slightly better.
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.
Czech models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from MorfFlex CZ 161115 and DeriNet 1.2 and the PoS tagger is trained on Prague Dependency Treebank 3.0 (PDT). and This work has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013).
The Czech morphologic system was devised by Jan Hajič.
The MorfFlex CZ dictionary was created by Jan Hajič and Jaroslava Hlaváčová.
The morphologic guesser research was supported by the projects 1ET101120503 and 1ET101120413 of Academy of Sciences of the Czech Republic and 100008/2008 of Charles University Grant Agency. The research was performed by Jan Hajič, Jaroslava Hlaváčová and David Kolovratník.
The tagger algorithm and feature set research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, GA405/09/0278 of the Grant Agency of the Czech Republic and 1ET101120503 of Academy of Sciences of the Czech Republic. The research was performed by Drahomíra "johanka" Spoustová, Jan Hajič, Jan Raab and Miroslav Spousta.
The tagger is trained on morphological layer of Prague Dependency Treebank PDT 2.5, which was supported by the projects LM2010013, LC536, LN00A063 and MSM0021620838 of Ministry of Education, Youth and Sports of the Czech Republic, and developed by Martin Buben, Jan Hajič, Jiří Hana, Hana Hanová, Barbora Hladká, Emil Jeřábek, Lenka Kebortová, Kristýna Kupková, Pavel Květoň, Jiří Mírovský, Andrea Pfimpfrová, Jan Štěpánek and Daniel Zeman.
Czech models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from MorfFlex CZ 2.0, DeriNet 2.1 and the PoS tagger is trained on Prague Dependency Treebank - Consolidated 1.0. and This work has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013).
The Czech morphologic system was devised by Jan Hajič.
The MorfFlex CZ dictionary was created by Jan Hajič and Jaroslava Hlaváčová.
The morphologic guesser research was supported by the projects 1ET101120503 and 1ET101120413 of Academy of Sciences of the Czech Republic and 100008/2008 of Charles University Grant Agency. The research was performed by Jan Hajič, Jaroslava Hlaváčová and David Kolovratník.
The tagger algorithm and feature set research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, GA405/09/0278 of the Grant Agency of the Czech Republic and 1ET101120503 of Academy of Sciences of the Czech Republic. The research was performed by Drahomíra "johanka" Spoustová, Jan Hajič, Jan Raab and Miroslav Spousta.
The tagger is trained on morphological layer of Prague Dependency Treebank PDT 2.5, which was supported by the projects LM2010013, LC536, LN00A063 and MSM0021620838 of Ministry of Education, Youth and Sports of the Czech Republic, and developed by Martin Buben, Jan Hajič, Jiří Hana, Hana Hanová, Barbora Hladká, Emil Jeřábek, Lenka Kebortová, Kristýna Kupková, Pavel Květoň, Jiří Mírovský, Andrea Pfimpfrová, Jan Štěpánek and Daniel Zeman.
Tokenizer, POS Tagger, Lemmatizer, and Parser model based on the PDT-C 1.0 treebank (https://hdl.handle.net/11234/1-3185). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model . To use these models, you need UDPipe version 2.1, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .