Czech subjectivity lexicon, i.e. a list of subjectivity clues for sentiment analysis in Czech. The list contains 4626 evaluative items (1672 positive and 2954 negative) together with their part of speech tags, polarity orientation and source information.
The core of the Czech subjectivity lexicon has been gained by automatic translation of a freely available English subjectivity lexicon downloaded from http://www.cs.pitt.edu/mpqa/subj_lexicon.html. For translating the data into Czech, we used parallel corpus CzEng 1.0 containing 15 million parallel sentences (233 million English and 206 million Czech tokens) from seven different types of sources automatically annotated at surface and deep layers of syntactic representation. Afterwards, the lexicon has been manually refined by an experienced annotator. and The work on this project has been supported by the GAUK 3537/2011 grant and by SVV project number 267 314.
The corpus contains video files of Czech Television News Broadcasts and JSON files with annotations of faces that appear in the broadcasts. The annotations are composed of frames in which a face is seen, name of the person whose face is seen, gender of the person (male/female), and the image region containing the face. The intended use of the corpus is to train models of faces for face detection, face identification, face verification, and face tracking. For convinience two different JSON files are provided. They contain the same data, but in different arrangements. One file has the identity of the person on the top, the other has the object ID on the top, where the object is a facetrack. A demo python skript is available for showing how to access the data.
BASIC INFORMATION
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Czech Text Document Corpus v 2.0 is a collection of text documents for automatic document classification in Czech language. It is composed of the text documents provided by the Czech News Agency and is freely available for research purposes. This corpus was created in order to facilitate a straightforward comparison of the document classification approaches on Czech data. It is particularly dedicated to evaluation of multi-label document classification approaches, because one document is usually labelled with more than one label. Besides the information about the document classes, the corpus is also annotated at the morphological layer.
The main part (for training and testing) is composed of 11,955 real newspaper articles. We provide also a development set which is intended to be used for tuning of the hyper-parameters of the created models. This set contains 2735 additional articles.
The total category number is 60 out of which 37 most frequent ones are used for classification. The reason of this reduction is to keep only the classes with the sufficient number of occurrences to train the models.
Technical Details
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Text documents are stored in the individual text files using UTF-8 encoding. Each filename is composed of the serial number and the list of the categories abbreviations separated by the underscore symbol and the .txt suffix. Serial numbers are composed of five digits and the numerical series starts from the value one.
For instance the file 00046_kul_nab_mag.txt represents the document file number 46 annotated by the categories kul (culture), nab (religion) and mag (magazine selection). The content of the document, i.e. the word tokens, is stored in one line. The tokens are separated by the space symbols.
Every text document was further automatically mophologically analyzed. This analysis includes lemmatization, POS tagging and syntactic parsing. The fully annotated files are stored in .conll files. We also provide the lemmatized form, file with suffix .lemma, and appropriate POS-tags, see .pos files. The tokenized version of the documents is also available in .tok files.
This corpus is available only for research purposes for free. Commercial use in any form is strictly excluded.
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 EBUContentGenre is a thesaurus containing the hierarchical description of various genres utilized in the TV broadcasting industry. This thesaurus is a part of a complex metadata specification called EBUCore intended for multifaceted description of audiovisual content. EBUCore (http://tech.ebu.ch/docs/tech/tech3293v1_3.pdf) is a set of descriptive and technical metadata based on the Dublin Core and adapted to media. EBUCore is the flagship metadata specification of European Broadcasting Union, the largest professional association of broadcasters around the world. It is developed and maintained by EBU's Technical Department (http://tech.ebu.ch). The translated thesaurus can be used for effective cataloguing of (mostly TV) audiovisual content and consequent development of systems for automatic cataloguing (topic/genre detection). and Technology Agency of the Czech Republic, project No. TA01011264
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)
A slightly modified version of the Czech Wordnet. This is the version used to annotate "The Lexico-Semantic Annotation of PDT using Czech WordNet": http://hdl.handle.net/11858/00-097C-0000-0001-487A-4
The Czech WordNet was developed by the Centre of Natural Language Processing at the Faculty of Informatics, Masaryk University, Czech Republic.
The Czech WordNet captures nouns, verbs, adjectives, and partly adverbs, and contains 23,094 word senses (synsets). 203 of these were created or modified by UFAL during correction of annotations. This version of WordNet was used to annotate word senses in PDT: http://hdl.handle.net/11858/00-097C-0000-0001-487A-4
A more recent version of Czech WordNet is distributed by ELRA: http://catalog.elra.info/product_info.php?products_id=1089 and 1ET201120505, LM2010013
CzEng 1.0 is the fourth release of a sentence-parallel Czech-English corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL) freely available for non-commercial research purposes.
CzEng 1.0 contains 15 million parallel sentences (233 million English and 206 million Czech tokens) from seven different types of sources automatically annotated at surface and deep (a- and t-) layers of syntactic representation. and EuroMatrix Plus (FP7-ICT-2007-3-231720 of the EU and 7E09003+7E11051 of the Ministry of Education, Youth and Sports of the Czech Republic),
Faust (FP7-ICT-2009-4-247762 of the EU and 7E11041 of the Ministry of Education, Youth and Sports of the Czech Republic),
GAČR P406/10/P259,
GAUK 116310,
GAUK 4226/2011
Czech-Slovak parallel corpus consisting of several freely available corpora (Acquis [1], Europarl [2], Official Journal of the European Union [3] and part of OPUS corpus [4] – EMEA, EUConst, KDE4 and PHP) and downloaded website of European Commission [5]. Corpus is published in both in plaintext format and with an automatic morphological annotation.
References:
[1] http://langtech.jrc.it/JRC-Acquis.html/
[2] http://www.statmt.org/europarl/
[3] http://apertium.eu/data
[4] http://opus.lingfil.uu.se/
[5] http://ec.europa.eu/ and This work has been supported by the grant Euro-MatrixPlus (FP7-ICT-2007-3-231720 of the EU and 7E09003 of the Czech Republic)