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.
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving three NLP tasks: machine translation, image captioning, and sentiment analysis.
The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks.
The models are described in the accompanying paper.
The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd
There are several separate ZIP archives here, each containing one model solving one of the tasks for one language.
To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey
To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory.
Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization).
The 'experiment.ini' file, which was used to train the model, is also included.
Then there are files containing the model itself, files containing the input and output vocabularies, etc.
For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/
For the machine translation, you do not need to tokenize the data, as this is done by the model.
For image captioning, you need to:
- download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
- clone the git repository with TensorFlow models: https://github.com/tensorflow/models
- preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script
Feel free to contact the authors of this submission in case you run into problems!
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving four NLP tasks: machine translation, image captioning, sentiment analysis, and summarization.
The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks.
The models are described in the accompanying paper.
The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd
In addition to the models presented in the referenced paper (developed and published in 2018), we include models for automatic news summarization for Czech and English developed in 2019. The Czech models were trained using the SumeCzech dataset (https://www.aclweb.org/anthology/L18-1551.pdf), the English models were trained using the CNN-Daily Mail corpus (https://arxiv.org/pdf/1704.04368.pdf) using the standard recurrent sequence-to-sequence architecture.
There are several separate ZIP archives here, each containing one model solving one of the tasks for one language.
To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey
To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory.
Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization).
The 'experiment.ini' file, which was used to train the model, is also included.
Then there are files containing the model itself, files containing the input and output vocabularies, etc.
For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/
For the machine translation, you do not need to tokenize the data, as this is done by the model.
For image captioning, you need to:
- download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
- clone the git repository with TensorFlow models: https://github.com/tensorflow/models
- preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script
The summarization models require input that is tokenized with Moses Tokenizer (https://github.com/alvations/sacremoses) and lower-cased.
Feel free to contact the authors of this submission in case you run into problems!
The Czech Legal Text Treebank (CLTT) is a collection of 1133 manually annotated dependency trees. CLTT consists of two legal documents: The Accounting Act (563/1991 Coll., as amended) and Decree on Double-entry Accounting for undertakers (500/2002 Coll., as amended).
The Czech Legal Text Treebank 2.0 (CLTT 2.0) annotates the same texts as the CLTT 1.0. These texts come from the legal domain and they are manually syntactically annotated. The CLTT 2.0 annotation on the syntactic layer is more elaborate than in the CLTT 1.0 from various aspects. In addition, new annotation layers were added to the data: (i) the layer of accounting entities, and (ii) the layer of semantic entity relations.
A lexicographical project, whose aim is to digitize and align two Czech onomasiological dictionaries (Haller 1969–77; Klégr 2007) in order to create an integrated digital multi-purpose lexico-semantic database of Czech.
The package contains Czech recordings of the Visual History Archive which consists of the interviews with the Holocaust survivors. The archive consists of audio recordings, four types of automatic transcripts, manual annotations of selected topics and interviews' metadata. The archive totally contains 353 recordings and 592 hours of interviews.
Czech models for NameTag, providing recognition of named entities.
The models are trained on Czech Named Entity Corpus 2.0 and 1.1. 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).
Czech models are trained on Czech Named Entity Corpus, which was created by Magda Ševčíková, Zdeněk Žabokrtský, Jana Straková and Milan Straka.
The recognizer research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, 1ET101120503 of Academy of Sciences of the Czech Republic, LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013), and partially by SVV project number 267 314. The research was performed by Jana Straková, Zdeněk Žabokrtský and Milan Straka.
Czech models use MorphoDiTa as a tagger and lemmatizer, therefore MorphoDiTa Acknowledgements (http://ufal.mff.cuni.cz/morphodita#morphodita_acknowledgements) and Czech MorphoDiTa Model Acknowledgements (http://ufal.mff.cuni.cz/morphodita/users-manual#czech-morfflex-pdt_acknowledgements) apply.
Czech models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from MorfFlex CZ and the PoS tagger is trained on PDT (Prague Dependency Treebank). 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 160310 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 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.
The Czech models for Korektor 2 created by Michal Richter, 02 Feb 2013. The models can either perform spellchecking and grammarchecking, or only generate diacritical marks. and This work was created by Michal Richter as an extension of his diploma thesis Advanced Czech Spellchecker. The models utilize MorfFlex CZ dictionary (http://hdl.handle.net/11858/00-097C-0000-0015-A780-9) created by Jan Hajič and Jaroslava Hlaváčová.
The dataset contains 4731 frozen continuous Czech multiword expressions. Inflectional word forms are generated for those MWEs where applicable. In total, the dataset contains 24,807 MWE forms.
The presented Czech Named Entity Corpus 1.0 is the first publicly available corpus providing a large body of manually annotated named entities in Czech sentences, including a fine-grained classification. and 1ET101120503 (Integrace jazykových zdrojů za účelem extrakce informací z přirozených textů)
Czech Named Entity Corpus 1.1 fixes some issues of the Czech Named Entity Corpus 1.0: misannotated entities are fixed, all formats contain the same data, tmt format is replaced with treex format, all formats contain splitting into training, development and testing portion of the data. and SVV 267 314 (Teoretické základy informatiky a výpočetní lingvistiky), LM2010013 (LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat), GPP406/12/P175 (Vybrané derivační vztahy pro automatické zpracování češtiny), PRVOUK (PRVOUK)
Czech Named Entity Corpus 2.0 is a corpus of 8993 Czech sentences with manually annotated 35220 Czech named entities, classified according to a two-level hierarchy of 46 named entities. and SVV 267 314 (Teoretické základy informatiky a výpočetní lingvistiky), LM2010013 (LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat), GPP406/12/P175 (Vybrané derivační vztahy pro automatické zpracování češtiny), PRVOUK (PRVOUK)
Czech OOV Inflection Dataset is a Czech inflection dataset of nouns, focused on evaluation in out-of-vocabulary (OOV) conditions. It consists of two parts: a standard lemma-disjoint train-dev-test split of a subset of noun paradigms of existing morphological dictionary Czech MorfFlex 2.0 (files train, dev and test-MorfFlex); and small set of neologisms from Čeština 2.0, annotated for inflected forms (file test-neologisms).
The corpus consists of recordings from the Chamber of Deputies of the Parliament of the Czech Republic. It currently consists of 88 hours of speech data, which corresponds roughly to 0.5 million tokens. The annotation process is semi-automatic, as we are able to perform the speech recognition on the data with high accuracy (over 90%) and consequently align the resulting automatic transcripts with the speech. The annotator’s task is then to check the transcripts, correct errors, add proper punctuation and label speech sections with information about the speaker. The resulting corpus is therefore suitable for both acoustic model training for ASR purposes and training of speaker identification and/or verification systems. The archive contains 18 sound files (WAV PCM, 16-bit, 44.1 kHz, mono) and corresponding transcriptions in XML-based standard Transcriber format (http://trans.sourceforge.net)
The date of airing of a particular recording is encoded in the filename in the form SOUND_YYMMDD_*. Note that the recordings are usually aired in the early morning on the day following the actual Parliament session. If the recording is too long to fit in the broadcasting scheme, it is divided into several parts and aired on the consecutive days.
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 .
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.
This is a dataset for natural language generation (NLG) in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015).
It includes input dialogue acts and the corresponding output natural language paraphrases in Czech. Since the dataset is intended for recurrent neural network based NLG systems using delexicalization, inflection tables for all slot values appearing verbatim in the text are provided.
The Czech RST Discourse Treebank 1.0 (CzRST-DT 1.0) is a dataset of 54 Czech journalistic texts manually annotated using the Rhetorical Structure Theory (RST). Each text document in the treebank is represented as a single tree-like structure, the nodes (discourse units) are interconnected through hierarchical rhetorical relations.
The dataset also contains concurrent annotations of five double-annotated documents.
The original texts are a part of the data annotated in the Prague Dependency Treebank, although the two projects are independent.
The corpus contains Czech expressive speech recorded using scenario-based approach by a professional female speaker. The scenario was created on the basis of previously recorded natural dialogues between a computer and seniors. and European Commission Sixth Framework Programme
Information Society Technologies Integrated Project IST-34434
Selected research articles and essays published in Czech Sociological Review from 1993 to 2016. Originally Czech, non-translated material only. 522 documents in total.
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
Lexicon of Czech verbal multiword expressions (VMWEs) used in Parseme Shared Task 2017. https://typo.uni-konstanz.de/parseme/index.php/2-general/142-parseme-shared-task-on-automatic-detection-of-verbal-mwes
Lexicon consists of 4785 VMWEs, categorized into four categories according to Parseme Shared Task (PST) typology: IReflV (inherently reflexive verbs), LVC (light verb constructions), ID (idiomatic expressions) and OTH (other VMWEs with other than verbal syntactic head).
Verbal multiword expressions as well as deverbative variants of VMWEs were annotated during the preparation phase of PST. These data were published as http://hdl.handle.net/11372/LRT-2282. Czech part includes 14,536 VMWE occurences:
1611 ID
10000 IReflV
2923 LVC
2 OTH
This lexicon was created out of Czech data. Each lexicon entry is represented by one line in the form:
type lemmas frequency PoS [used form 1; used form 2; ... ]
(columns are separated by tabs) where:
type ... is the type of VMWE in PST typology
lemmas ... are space separated lemmatized forms of all words that constitutes the VMWE
frequency ... is the absolute frequency of this item in PST data
PoS ... is a space separated list of parts of speech of individual words (in the same order as in "lemmas")
final field contains a list of all (1 to 18) used forms found in the data (since Czech is a flective language).
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
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 4A, B from 1945 shows how, in early 1945, Czech agricultural youth were involved in digging trenches as a part of their forced labour (Totaleinsatz). Their work was supervised by instructors of the Board of Trustees for the Education of Youth. General Secretary of the Board František Teuner arrived to inspect their progress.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 32B from 1943 was shot during an event organised by the Board of Trustees for the Education of Youth in the summer of 1943. Czech youth helped with harvesting as part of their mandatory service.
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)
CzeDLex 0.5 is a pilot version of a lexicon of Czech discourse connectives. The lexicon contains connectives partially automatically extracted from the Prague Discourse Treebank 2.0 (PDiT 2.0), a large corpus annotated manually with discourse relations. The most frequent entries in the lexicon (covering more than 2/3 of the discourse relations annotated in the PDiT 2.0) have been manually checked, translated to English and supplemented with additional linguistic information.
CzeDLex 0.6 is the second development version of the lexicon of Czech discourse connectives. The lexicon contains connectives partially automatically extracted from the Prague Discourse Treebank 2.0 (PDiT 2.0), a large corpus annotated manually with discourse relations. The most frequent entries in the lexicon (76 out of total 204 entries, covering more than 90% of the discourse relations annotated in PDiT 2.0), have been manually checked, translated to English and supplemented with additional linguistic information.
CzeDLex 0.7 is the third development version of the Lexicon of Czech discourse connectives. The lexicon contains connectives partially automatically extracted from the Prague Discourse Treebank 2.0 (PDiT 2.0) and, as a supplementary resource, the Czech part of the Prague Czech–English Dependency Treebank with discourse annotation projected from the Penn Discourse Treebank 3.0. The most frequent entries in the lexicon (131 out of total 218 entries, covering more than 95% of discourse relations annotated in PDiT 2.0), have been manually checked, translated to English and supplemented with additional linguistic information.
CzeDLex 1.0 is the first production version (the fourth development version) of the Lexicon of Czech discourse connectives. The lexicon contains connectives partially automatically extracted from resources annotated manually with discourse relations: the Prague Discourse Treebank 2.0 (PDiT 2.0) as the primary resource, and two supplementary resources: (i) the Czech part of the Prague Czech–English Dependency Treebank with discourse annotation projected from the Penn Discourse Treebank 3.0, and (ii) a thousand sentences selected from various fiction novels and transcriptions of public speeches. All 200 entries in the lexicon have been manually checked, translated to English and supplemented with additional linguistic information.
CzEng 0.7 is a Czech-English parallel corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL), Charles University, Prague. The corpus contains no manual annotation. It is limited only to texts which have been already available in an electronic form and which are not protected by authors' rights in the Czech Republic. The main purpose of the corpus is to support Czech-English and English-Czech machine translation research with the necessary data. CzEng 0.7 consists of a large set of parallel textual documents mainly from the fields of European law, information technology, and fiction, all of them converted into a uniform XML-based file format and provided with automatic sentence alignment.
The CzEngClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and Czech (http://hdl.handle.net/11858/00-097C-0000-0001-4880-3) and English Wordnets (https://wordnet.princeton.edu/). Part of the dataset is a file reflecting annotators choices for assignment of verbs to classes.
The CzEngClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and Czech (http://hdl.handle.net/11858/00-097C-0000-0001-4880-3) and English Wordnets (https://wordnet.princeton.edu/). Part of the dataset are files reflecting annotators choices and agreement for assignment of verbs to classes.
The CzEngClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and Czech (http://hdl.handle.net/11858/00-097C-0000-0001-4880-3) and English Wordnets (https://wordnet.princeton.edu/).
CzEngVallex is a bilingual valency lexicon of corresponding Czech and English verbs. It connects 20835 aligned valency frame pairs (verb senses) which are translations of each other, aligning their arguments as well. The CzEngVallex serves as a powerful, real-text-based database of frame-to-frame and subsequently argument-to-argument pairs and can be used for example for machine translation applications. It uses the data from the Prague Czech-English Dependency Treebank project (PCEDT 2.0, http://hdl.handle.net/11858/00-097C-0000-0015-8DAF-4) and it also takes advantage of two existing valency lexicons: PDT-Vallex for Czech and EngVallex for English, using the same view of valency (based on the Functional Generative Description theory). The CzEngVallex is available in an XML format in the LINDAT/CLARIN repository, and also in a searchable form (see the “More Apps” tab) interlinked with PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F),EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2) and with examples from the PCEDT.
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.
Neusatz und Faksimile der zehnbändigen Ausgabe (Leipzig, 1834-1838); wortgenaue Seitenkonkordanz zu der gedruckten Ausgabe; Darstellung der Gegenstandsbereiche gesellschaftlicher Konversation (speziell auf eine weibliche Zielgruppe ausgerichtet)
We present DaMuEL, a large Multilingual Dataset for Entity Linking containing data in 53 languages. DaMuEL consists of two components: a knowledge base that contains language-agnostic information about entities, including their claims from Wikidata and named entity types (PER, ORG, LOC, EVENT, BRAND, WORK_OF_ART, MANUFACTURED); and Wikipedia texts with entity mentions linked to the knowledge base, along with language-specific text from Wikidata such as labels, aliases, and descriptions, stored separately for each language. The Wikidata QID is used as a persistent, language-agnostic identifier, enabling the combination of the knowledge base with language-specific texts and information for each entity. Wikipedia documents deliberately annotate only a single mention for every entity present; we further automatically detect all mentions of named entities linked from each document. The dataset contains 27.9M named entities in the knowledge base and 12.3G tokens from Wikipedia texts. The dataset is published under the CC BY-SA licence.
Danish Fungi 2020 (DF20) is a fine-grained dataset and benchmark. The dataset, constructed from observations submitted to the Danish Fungal Atlas, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy. DF20 has zero overlap with ImageNet, allowing unbiased comparison of models fine-tuned from publicly available ImageNet checkpoints.
The dataset has 1,604 different classes, with 248,466 training images and 27,608 test images.
Register of decrees as well as texts on the history of Prussia and the Teutonic Order; Regesten und Texte zur Geschichte Preußens und des Deutschen Ordens
The database contains about 5 Million dialectal linguistic evidences collected in differend projects within the Free State of Bavaria to the dialects Bavarian, Frankish, and Swabian.
In 1984, linguists at the University of Augsburg began to collect dialect data for the research and documentation project "Linguistic Map of Swabia" (German: "Sprachatlas von Bayerisch-Schwaben (SBS)"). In 1986, the University of Bayreuth followed with preparations for the "Linguistic Map of North- and East-Bavaria" (German: "Sprachatlas von Nordostbayern (SNOB)"). In the following years, partner projects of the other regions also started to collect data in their particular region. All six language projects then formed the "Research Association of the Bavarian Linguistic Map " (German: Bayerischer Sprachatlas (BSA)"), which was funded by the DFG and the Bavarian State Ministry of Science, Research and the Arts.
The first digital publication of BayDat by Ralf Zimmermann in 2007 at the University of Würzburg (see linked paper) was re-designed in 2019 by Manuel Raaf at the Bavarian Academy of Sciences and Humanities.
For detailed information, please see https://baydat.badw.de/info
The corpus contains Czech speech of laryngectomy patients recorded before a surgery causing their voice to be lost in order to preserve the voice which can be later used for personalized text-to-speech system. Individual utterances were selected from the language by a special algorithm to cover as much phonetic and prosodic features as possible.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 38A from 1943 contains footage from the Days of Czech Youth event organised by the Board of Trustees for the Education of Youth from 11 to 12 September. A concert of three brass bands, led by Miloš Kuba, and the Kühn Children´s Choir was held on Peace Square at 5 pm on 11 September. A procession of the Board´s members set out from Peace Square and continued through the streets of Prague. The event culminated with a track and field championship at Strahov Stadium where the winners of district rounds competed against each other. The spectators were welcomed by General Secretary of the Board František Teuner. The programme included a dance performance by girls in folk costumes. The event concluded with a speech by Minister of Education and People´s Enlightenment and Chairman of the Board Emanuel Moravec, followed by a solemn oath "to the Führer and to the Fatherland".
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 35A from 1943 captures the mood of the District Youth Track and Field Championship for Ages 10-18, which was organised by the Board of Trustees for the Education of Youth in eighty towns of the Protectorate as part of the Days of Czech Youth event held from 28 to 29 August 1943. At the A. F. K. Stadium in Kolín nad Labem, approximately 1,500 athletes qualified for the Track and Field Championship of Bohemia and Moravia.
This corpus contains the text of De Latinae Linguae Reparatione authored by Marcus Antonius Sabellicus (1436–1506), annotated with respect to lemmas, part-of-speech tags, morphological features and syntactic dependencies according to the typological formalism of Universal Dependencies (UD).
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 17B from 1945 shows a competition for the best decorated Easter egg, which was organised by girls from the Moravian Slovak branch of the Board of Trustees for the Education of Youth as part of the youth service of honour. Local women artisans, skilled in the traditional techniques, helped them with painting and etching patterns on Easter eggs.
The Sequoia corpus is a set of 3,099 linguistically-annotated French sentences, originating from four sources (Europarl, European Agency Reports, French regional journal L'Est Républicain, and French wikipedia).
Several types of annotations were added over the years.
The current release comprises:
- parts-of-speech (SEQUOIA ANR-08-EMER-013 project)
- syntactic dependency trees
- deep syntactic dependency graphs (Deep sequoia project)
- multi-word expressions and named entities (PARSEME COST project and PARSEME-FR ANR-14-CERA-0001 project)
- coarse semantic tags for nouns (FrSemCor project)
See the deep sequoia page for a detailed description: https://deep-sequoia.inria.fr/
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-2988). 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/11234/1-3105). 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/11234/1-3226). 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.
The dataset contains delimitation of borders of dialect regions, subgroups, areas and types in the Czech Republic. It is the result of an extensive expert revision that was based on various sources and made the delimitation exact and accurate. At the same time, the dataset corresponds to the underlying data of the Mapka application running at https://korpus.cz/mapka/
There are four files in this submission. Two files contain the delimitation of dialect regions ("oblasti"; both in GeoJSON and Shapefile formats) and two files contain the delimitation of smaller dialect areas, i.e. subgroups, areas and types ("oblasti_jemne"; again in GeoJSON and Shapefile formats).
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Changes in version 1.1:
1. Universal Dependencies tagset instead of the older and smaller Google Universal POS tagset.
2. SVM classifier trained on Universal Dependencies 1.2 instead of HamleDT 2.0.
3. Balto-Slavic languages, Germanic languages and Romance languages were tagged by classifier trained only on the respective group of languages. Other languages were tagged by a classifier trained on all available languages. The "c7" combination from version 1.0 is no longer used.
The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 43A captures the demobilisation of the Czechoslovak Army after 9 October 1938. Conscripted soldiers are handing in their equipment and leaving the barracks.
Coordinates work of a group of linguists selecting appropriate parse trees from many generated ones. Assigns parts of the task, signalling differences in annotation and allowing them to be corrected by a supervisor.
DeriNet is a lexical network which contains derivational relations in Czech modeled as an oriented graph. Nodes correspond to Czech lexemes (a lexeme is a single lemma, possibly with only a subset of its senses – homonyms may have different derivations and are thus represented by several lexemes) and edges represent derivations between them. DeriNet 1.0 contains 968,967 lexemes with 965,535 unique lemmas; connected by 715,729 derivational links. Lexemes in DeriNet 1.0 are sampled from the MorfFlex dictionary.
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes (i.e. single lemmas, possibly with only a subset of their senses), edges represent derivational relations between a derived word and its base word. The present version, DeriNet 1.2, contains 1,003,590 lexemes (sampled from the MorfFlex dictionary) with 1,001,394 unique lemmas, connected by 740,750 derivational links. Both rather technical and linguistic changes were made as compared to the previous version of the data; e.g. new version of the MorfFlex dictionary was used, derived words that contain a consonant and/or vowel alternation (e.g. boží) were connected with their base word (bůh).
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent derivational relations between a derived word and its base word. The present version, DeriNet 1.5, contains 1,011,965 lexemes (sampled from the MorfFlex dictionary) connected by 785,543 derivational links. Besides several rather conservative updates (such as newly identified prefix and suffix verb-to-verb derivations as well as noun-to-adjective derivations manifested by most frequent adjectival suffixes), DeriNet 1.5 is the first version that contains annotations related to compounding (compound words are distinguished by a special mark in their part-of-speech labels).
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent derivational relations between a derived word and its base word. The present version, DeriNet 1.6, contains 1,027,832 lexemes (sampled from the MorfFlex dictionary) connected by 803,404 derivational links. Furthermore, starting with version 1.5, DeriNet contains annotations related to compounding (compound words are distinguished by a special mark in their part-of-speech labels).
Compared to version 1.5, version 1.6 was expanded by extracting potential links from dictionaries available under suitable licences, such as Wiktionary, and by enlarging the number of marked compounds.
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent derivational or compositional relations between a derived word and its base word / words. The present version, DeriNet 2.0, contains 1,027,665 lexemes (sampled from the MorfFlex dictionary) connected by 808682 derivational and 600 compositional links.
Compared to previous versions, version 2.0 uses a new format and contains new types of annotations: compounding, annotation of several morphological and other categories of lexemes, identification of root morphs of 244,198 lexemes, semantic labelling of 151,005 relations using five labels and identification of 13 fictitious lexemes.
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent word-formational relations between a derived word and its base word / words. The present version, DeriNet 2.1, contains 1,039,012 lexemes (sampled from the MorfFlex CZ 2.0 dictionary) connected by 782,814 derivational, 50,533 orthographic variant, 1,952 compounding, 295 univerbation and 144 conversion relations.
Compared to the previous version, version 2.1 contains annotations of orthographic variants, full automatically generated annotation of affix morpheme boundaries (in addition to the roots annotated in 2.0), 202 affixoid lexemes serving as bases for compounding, annotation of corpus frequency of lexemes, annotation of verbal conjugation classes and a pilot annotation of univerbation. The set of part-of-speech tags was converted to Universal POS from the Universal Dependencies project.
A dictionary of old legal German. Includes words up until 1800. Historisches Wörterbuch; Dokumentation von Rechtswörtern sowie Wörtern mit rechtlichen Bezügen (bis etwa 1800)
written general monolingual synchronic (1959-) reference corpus archive; 5.4 billion words; structural information down to sentence level, rich bibliographic metadata, partial layout information, fully morpho-syntactically annotated
Angabe von grammatischen Informationen, Worterklärungen, typischen (syntaktischen) Verbindungen, idiomatischen Wendungen und Beispielsätzen; Möglichkeit, sich Übersetzungen des jeweiligen Wortes anzeigen zu lassen
A parsed corpus of spoken English. Ca 400,000 words from ICE-GB (early 1990s) and 400,000 words from the London-Lund Corpus (late 1960s-early 1980s). The orthographic transcriptions have been normalised and annotated.
Diachronic corpus of Czech sized 3.45 million words (i.e. 4.1 million tokens). It contains 116 texts from the 14th-20th century period. The texts are transcribed, not transliterated. Diakorp v6 is provided in a CoNLL-U-like vertical format used as an input to the Manatee query engine. The data thus correspond to the corpus available via the KonText query interface to the registered users of CNC at http://www.korpus.cz
The Dialogy.Org system allows users to search in transcribed audio-visual corpora. The Dialogy.Org works on the principle of web-based interface, so installation of additional programs on your computer is not necessary. You must have Flash Player for playing audio or video recordings. 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, Youth and Sports of the Czech Republic (project LM2010013).