CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.2 consists of 25 datasets for 16 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 21 datasets for 15 languages (1 dataset for Ancient Greek, 1 for Ancient Hebrew, 1 for Catalan, 2 for Czech, 3 for English, 1 for French, 2 for German, 2 for Hungarian, 1 for Lithuanian, 2 for Norwegian, 1 for Old Church Slavonic, 1 for Polish, 1 for Russian, 1 for Spanish, and 1 for Turkish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource, too. Compared to the previous version 1.1, the version 1.2 comprises new languages and corpora, namely Ancient_Greek-PROIEL, Ancient_Hebrew-PTNK, English-LitBank, and Old_Church_Slavonic-PROIEL. In addition, English-GUM and Turkish-ITCC have been updated to newer versions, conversion of zeros in Polish-PCC has been improved, and the conversion pipelines for multiple other datasets have been refined (a list of all changes in each dataset can be found in the corresponding README file).
CUBBITT En-Cs translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2014 (BLEU):
en->cs: 27.6
cs->en: 34.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
This is a parallel corpus of Czech and mostly English abstracts of scientific papers and presentations published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague. For each publication record, the authors are obliged to provide both the original abstract (in Czech or English), and its translation (English or Czech) in the internal Biblio system. The data was filtered for duplicates and missing entries, ensuring that every record is bilingual. Additionally, records of published papers which are indexed by SemanticScholar contain the respective link. The dataset was created from September 2022 image of the Biblio database and is stored in JSONL format, with each line corresponding to one record.
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.
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 Czech Web Corpus 2017 (csTenTen17) is a Czech corpus made up of texts collected from the Internet, mostly from the Czech national top level domain ".cz". The data was crawled by web crawler SpiderLing (https://corpus.tools/wiki/SpiderLing).
The data was cleaned by removing boilerplate (using https://corpus.tools/wiki/Justext), removing near-duplicate paragraphs (by https://corpus.tools/wiki/Onion) and discarding paragraphs not in the target language.
The corpus was POS annotated by morphological analyser Majka using this POS tagset: https://www.sketchengine.eu/tagset-reference-for-czech/.
Text sources: General web, Wikipedia.
Time span of crawling: May, October and November 2017, October and November 2016, October and November 2015. The Czech Wikipedia part was downloaded in November 2017.
Data format: Plain text, vertical (one token per line), gzip compressed. There are the following structures in the vertical: Documents (<doc/>, usually corresponding to web pages), paragraphs (<p/>), sentences (<s/>) and word join markers (<g/>, a "glue" tag indicating that there was no space between the surrounding tokens in the original text). Document metadata: src (the source of the data), title (the title of the web page), url (the URL of the document), crawl_date (the date of downloading the document). Paragraph metadata: heading ("1" if the paragraph is a heading, usually <h1> to <h6> elements in the original HTML data). Block elements in the case of an HTML source or double blank lines in the case of other source formats were used as paragraph separators. An internal heuristic tool was used to mark sentence breaks. The tab-separated positional attributes are: word form, morphological annotation, lem-POS (the base form of the word, i.e. the lemma, with a part of speech suffix) and gender respecting lemma (nouns and adjectives only).
Please cite the following paper when using the corpus for your research: Suchomel, Vít. csTenTen17, a Recent Czech Web Corpus. In Recent Advances in Slavonic Natural Language Processing, pp. 111–123. 2018. (https://nlp.fi.muni.cz/raslan/raslan18.pdf#page=119)
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3226). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3424). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3687). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
ELITR Minuting Corpus consists of transcripts of meetings in Czech and English, their manually created summaries ("minutes") and manual alignments between the two.
Czech meetings are in the computer science and public administration domains and English meetings are in the computer science domain.
Each transcript has one or multiple corresponding minutes files. Alignments are only provided for a portion of the data.
This corpus contains 59 Czech and 120 English meeting transcripts, consisting of 71097 and 87322 dialogue turns respectively. For Czech meetings, we provide 147 total minutes with 55 of them aligned. For English meetings, it is 256 total minutes with 111 of them aligned.
Please find a more detailed description of the data in the included README and stats.tsv files.
If you use this corpus, please cite:
Nedoluzhko, A., Singh, M., Hledíková, M., Ghosal, T., and Bojar, O.
(2022). ELITR Minuting Corpus: A novel dataset for automatic minuting
from multi-party meetings in English and Czech. In Proceedings of the
13th International Conference on Language Resources and Evaluation
(LREC-2022), Marseille, France, June. European Language Resources
Association (ELRA). In print.
@inproceedings{elitr-minuting-corpus:2022,
author = {Anna Nedoluzhko and Muskaan Singh and Marie
Hled{\'{\i}}kov{\'{a}} and Tirthankar Ghosal and Ond{\v{r}}ej Bojar},
title = {{ELITR} {M}inuting {C}orpus: {A} Novel Dataset for
Automatic Minuting from Multi-Party Meetings in {E}nglish and {C}zech},
booktitle = {Proceedings of the 13th International Conference
on Language Resources and Evaluation (LREC-2022)},
year = 2022,
month = {June},
address = {Marseille, France},
publisher = {European Language Resources Association (ELRA)},
note = {In print.}
}
Ministerstvo školství, mládeže a tělovýchovy České republiky@@LM2018101@@LINDAT/CLARIAH-CZ: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy@@nationalFunds@@✖[remove]62