Segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1942, issue no. 17, captures the presentation of a gift Ï Ambulance Train no. 751 Ï from the Protectorate of Bohemia and Moravia to Adolf Hitler and the German army. The train handover took place at Prague Main Railway Station on 20 April 1942, the birthday of Adolf Hitler. Cars arrive in front of Prague Main Railway Station. Acting Reich Protector Reinhard Heydrich enters the train station. State President Emil Hácha gives a speech in the festively decorated railway hall. In response, Heydrich shakes his hand. The event is witnessed by a delegation of railway workers. The train crew lines up on the station platform. Heydrich enters the train with his entourage and inspects the sleeping cars, the operating carriage, the kitchen, and the sick bay. The inspection of the ambulance train is attended by Protectorate Prime Minister Jaroslav Krejčí and Minister of Education and People´s Enlightenment Emanuel Moravec. According to the voiceover, the train was made in a railway workshop in Prague-Bubny in record time. It consisted of 28 carriages and 20 hospital carriages, was 410 metres long, weighed 545 tons and had capacity for 280 wounded.
This is an open dataset of sentences from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains a corpus for language modeling and human annotations for named entity recognition (NER).
This is an open dataset of sentences from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains a corpus for language modeling and human annotations for named entity recognition (NER).
This is an open dataset of scanned images and OCR texts from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains human annotations for layout analysis, OCR evaluation, and language identification.
These are supplementary materials for an open dataset of scanned images and OCR texts from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains human annotations for layout analysis, OCR evaluation, and language identification and is available at http://hdl.handle.net/11234/1-4615. These supplementary materials contain OCR texts from different OCR engines for book pages for which we have both high-resolution scanned images and annotations for OCR evaluation.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) 1942, issue no. 27A, captures the Pledge of Czech Theatre Professionals´ Allegiance to the Reich, a manifestation held at the National Theatre in Prague on 25 June 1942, which was to unequivocally condemn the assassination of Acting Reich Protector Reinhard Heydrich. Speeches are delivered by actor Rudolf Deyl Jr. and Minister of Education and People´s Enlightenment Emanuel Moravec (silent). Actress Růžena Nasková and actors Karel Höger, Ferenc Futurista, and Stanislav Neumann are seen among the participants. The segment concludes with everyone performing the Nazi salute.
This small dataset contains 3 speech corpora collected using the Alex Translate telephone service (https://ufal.mff.cuni.cz/alex#alex-translate).
The "part1" and "part2" corpora contain English speech with transcriptions and Czech translations. These recordings were collected from users of the service. Part 1 contains earlier recordings, filtered to include only clean speech; Part 2 contains later recordings with no filtering applied.
The "cstest" corpus contains recordings of artificially created sentences, each containing one or more Czech names of places in the Czech Republic. These were recorded by a multinational group of students studying in Prague.
Segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1941, issue no. 40, captures events linked to the accession of SS-Obergruppenführer Reinhard Heydrich to the office of Deputy Reich Protector of the Protectorate of Bohemia and Moravia on 27 September 1941. Heydrich attends an SS military parade on Hradčanské Square in Prague. Military dignitaries and state officials welcome him in the first quadrangle of Prague Castle. The Nazi flag flies over Prague Castle. Reich Commissioner for the Sudetenland Konrad Henlein and Reich Secretary Karl Hermann Frank are present at the occasion. State President Emil Hácha receives Reinhard Heydrich at Prague Castle.
Additional three Czech reference translations of the whole WMT 2011 data set (http://www.statmt.org/wmt11/test.tgz), translated from the German originals. Original segmentation of the WMT 2011 data is preserved. and This project has been sponsored by the grants GAČR P406/11/1499 and EuroMatrixPlus (FP7-ICT-2007-3-231720 of the EU and 7E09003+7E11051 of the Ministry of Education, Youth and Sports of the Czech Republic)
Lexical network AdjDeriNet consists of pairs of base adjectives and their derivatives. It contains nearly 18 thousand base adjectives that are base words for more than 26 thousand lexemes of several parts of speech.
Painter Adolf Hoffmeister on Bohumil Veselý's balcony. Hoffmeister in a fragmented segment from Československé filmové noviny (Czechoslovak Film News) 1952, issue no. 26.
Corpus AKCES 2 consists of trancripts of recordings of classes at Czech elementary and secondary schools (AKCES/CLAC - Czech Language Acquisition Corpora). It is the same data as the corpus "Schola 2010" (see the link for search), but all the proper names have been removed in order to protect the privacy of participants. and MŠMT (MSM0021620825), UK (PRVOUK P 10)
Corpus AKCES 2 ver. 2 consists of full, unabridged trancripts of recordings of classes at Czech elementary and secondary schools (AKCES/CLAC - Czech Language Acquisition Corpora). It is the same data as the corpus "Schola 2010" (see the link for search), but all the proper names have been removed in order to protect the privacy of participants. and UK, PRVOUK P10
Corpus AKCES 3 includes texts written in czech by non-native speakers (AKCES/CLAC - Czech Language Acquisition Corpora) and ESF (OPVK CZ.1.07/2.2.00/07.0259), MŠMT (MSM0021620825), UK (P10)
Corpus AKCES 4 includes texts written in czech by youth growing up in locations at risk of social exclusion (AKCES/CLAC - Czech Language Acquisition Corpora) and ESF (OPVK CZ.1.07/2.2.00/07.0259), MŠMT (MSM0021620825), UK (P10)
Essays written by non-native learners of Czech, a part of AKCES/CLAC – Czech Language Acquisition Corpora. CzeSL-SGT stands for Czech as a Second Language with Spelling, Grammar and Tags. Extends the “foreign” (ciz) part of AKCES 3 (CzeSL-plain) by texts collected in 2013. Original forms and automatic corrections are tagged, lemmatized and assigned erros labels. Most texts have metadata attributes (30 items) about the author and the text.
Essays written by non-native learners of Czech, a part of AKCES/CLAC – Czech Language Acquisition Corpora. CzeSL-SGT stands for Czech as a Second Language with Spelling, Grammar and Tags. Extends the “foreign” (ciz) part of AKCES 3 (CzeSL-plain) by texts collected in 2013. Original forms and automatic corrections are tagged, lemmatized and assigned erros labels. Most texts have metadata attributes (30 items) about the author and the text.
In addition to a few minor bugs, fixes a critical issue in Release 1: the native speakers of Ukrainian (s_L1:"uk") were wrongly labelled as speakers of "other European languages" (s_L1_group="IE"), instead of speakers of a Slavic language (s_L1_group="S"). The file is now a regular XML document, with all annotation represented as XML attributes.
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}
}
Ornithologist Alfréd Hořice with his collection of stuffed birds in a fragmented segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1945, issue no. 19.
Cold water swimmer Alfréd Nikodém as the oldest participant in a swimming race in the Vltava River in a segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1942, issue no. 36. Nikodém with a bouquet of flowers by Svatopluk Čech Bridge.
Conductor Alois Klíma conducts the Prague Radio Symphony Orchestra at the Prague Spring International Music Festival in a segment from Československé filmové noviny (Czechoslovak Film News) 1952, issue no. 24. The orchestra performs Bedřich Smetana´s symphony Tábor.
The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 23 captures Peter Hletko´s speech on the importance of the Pittsburgh Agreement and continues with a report on the meeting between the five-member delegation of the American Slovak League, led by Peter Hletko, and President Edvard Beneš, which was held in Prague on 30 May 1938.
The PARSEME shared task aims at identifying verbal MWEs in running texts. Verbal MWEs include idioms (let the cat out of the bag), light verb constructions (make a decision), verb-particle constructions (give up), and inherently reflexive verbs (se suicider 'to suicide' in French). VMWEs were annotated according to the universal guidelines in 18 languages. The corpora are provided in the parsemetsv format, inspired by the CONLL-U format.
For most languages, paired files in the CONLL-U format - not necessarily using UD tagsets - containing parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe).
This item contains training and test data, tools and the universal guidelines file.
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
Every decision is annotated by two trained annotators and then manually adjudicated by one trained curator to solve possible disagreements between annotators. Adjudication was conducted non-destructively, therefore dataset contains all original annotations.
Corpus was developed as training and testing material for reference recognition tasks. Dataset contains references to other court decisions and literature. All references consist of basic units (identifier of court decision, identification of court issuing referred decision, author of book or article, title of book or article, point of interest in referred document etc.), values (polarity, depth of discussion etc.).
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
Every decision is annotated by two trained annotators and then manually adjudicated by one trained curator to solve possible disagreements between annotators. Adjudication was conducted non-destructively, therefore corpus (raw) contains all original annotations.
Corpus was developed as training and testing material for reference recognition tasks. Dataset contains references to other court decisions and literature. All references consist of basic units (identifier of court decision, identification of court issuing referred decision, author of book or article, title of book or article, point of interest in referred document etc.), values (polarity, depth of discussion etc.).
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
280 decisions were annotated by one trained annotator and then manually adjudicated by one trained curator. 70 decisions were annotated by two trained annotators and then manually adjudicated by one trained curator. Adjudication was conducted destructively, therefore dataset contains only the correct annotations and does not contain all original annotations.
Corpus was developed as training and testing material for text segmentation tasks. Dataset contains decision segmented into Header, Procedural History, Submission/Rejoinder, Court Argumentation, Footer, Footnotes, and Dissenting Opinion. Segmentation allows to treat different parts of text differently even if it contains similar linguistic or other features.
We defined 58 dramatic situations and annotated them in 19 play scripts. Then we selected only 5 well-recognized dramatic situations and annotated further 33 play scripts. In this version of the data, we release only play scripts that can be freely distributed, which is 9 play scripts. One play is annotated independently by three annotators.
Antonín Martin Brousil, the vice-chancellor of Prague's Academy of Performing Arts, and Mexican actress Rosaura Revueltas at the 1954 Karlovy Vary International Film Festival in a fragmented segment from the weekly film newsreel.
Painter Antonín Pelc with his wife Jarmila Záhořová in the studio in a segment from Československé filmové noviny (Czechoslovak Film News) 1952, issue no. 43. The painter in his studio on the day of his 60th birthday in a segment from Československý filmový týdeník (Czechoslovak Film Weekly Newsreel) 1955, issue no. 4.
Artificially created treebank of elliptical constructions (gapping), in the annotation style of Universal Dependencies. Data taken from UD 2.1 release, and from large web corpora parsed by two parsers. Input data are filtered, sentences are identified where gapping could be applied, then those sentences are transformed, one or more words are omitted, resulting in a sentence with gapping. Details in Droganova et al.: Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions, LREC 2018, Miyazaki, Japan.
This dataset contains a number of user product reviews which are publicly available on the website of an established Czech online shop with electronic devices. Each review consists of negative and positive aspects of the product. This setting pushes the customer to rate important characteristics.
We have selected 2000 positive and negative segments from these reviews and manually tagged their targets. Additionally, we selected 200 of the longest reviews and annotated them in the same way. The targets were either aspects of the evaluated product or some general attributes (e.g. price, ease of use).
This record contains audio recordings of proceedings of the Chamber of Deputies of the Parliament of the Czech Republic. The recordings have been provided by the official websites of the Chamber of Deputies, and the set contains them in their original format with no further processing.
Recordings cover all available audio files from 2013-11-25 to 2023-07-26. Audio files are packed by year (2013-2023) and quarter (Q1-Q4) in tar archives audioPSP-YYYY-QN.tar.
Furthermore, there are two TSV files: audioPSP-meta.quarterArchive.tsv contains metadata about archives, and audioPSP-meta.audioFile.tsv contains metadata about individual audio files.
This dataset contains automatic paraphrases of Czech official reference translations for the Workshop on Statistical Machine Translation shared task. The data covers the years 2011, 2013 and 2014.
For each sentence, at most 10000 paraphrases were included (randomly selected from the full set).
The goal of using this dataset is to improve automatic evaluation of machine translation outputs.
If you use this work, please cite the following paper:
Tamchyna Aleš, Barančíková Petra: Automatic and Manual Paraphrases for MT Evaluation. In proceedings of LREC, 2016.
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.
Sculptor Bohumil Kafka works on a statue of Josef Mánes in a fragmented segment from the Ufa žurnál (Ufa Journal) 1939, issue no. 200. The unveiling of the monument by the Rudolfinum, including a speech by Professor Vratislav Nechleba in a fragmented segment from Československé filmové noviny (Czechoslovak Film News) 1951, issue no. 52. Kafka at Prague Zoo working on a study of a lion for the Milan Rastislav Štefánik's monument in a segment from Československý filmový týdeník (Czechoslovak Film Weekly Newsreel) 1937, issue no. 5. Kafka with politician Milan Hodža in the artist´s studio in Prague-Dejvice.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
Relationship extraction models for the Czech language. Models are trained on CERED (dataset created by distant supervision on Czech Wikipedia and Wikidata) and recognize a subset of Wikidata relations (listed in CEREDx.LABELS).
We supply a demo.py that performs inference on user-defined input and requirements.txt file for pip. Adapt the demo code to use the model.
Both the dataset and the models are presented in Relationship Extraction thesis.
The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 33 shows children playing in gas masks in Prague.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 1A from 1944 was shot during a Christmas exhibition organised by the Board of Trustees for the Education of Youth and held in the hall of the Black Rose Palace in Na Příkopě Street in Prague from 18 to 22 December. The exhibition included a display of the 500 prettiest toys made as part of the Sewing Dolls initiative. Girls made 59,000 dolls, out of which 44,000 went to the children of the labourers working in the Reich and 15,000 to the children of the German soldiers fighting on the front. The exhibition was toured by Minister of Education and People´s Enlightenment and Chairman of the Board Emanuel Moravec and General Secretary of the Board František Teuner.
Transcripts of longitudinal audio recordings of 7 Czech typical monolingual children between 1;7 to 3;9. Files are in plain text with UTF-8 encoding. Each file represents one recording session of one of the target children and is named with the presudonym of the child and her age at the given session in form YMMDD. Transcription rules and other details are to find on the homepage coczefla.ff.cuni.cz.
A new version of the previously published corpus Chroma. The version 2023.04 includes six children. Two transcripts (Julie20221, Klara30424) were removed since they did not meet the criteria on the dialogical format. The transcripts were revised (eliminating typing errors and inconsistencies in the transcription format) and morphologically annotated by the automatic tool MorphoDiTa. Detailed manual control of the annotation was performed on children's utterances; the annotation of adult data was not checked yet. Files are in plain text with UTF-8 encoding. Each file represents one recording session of one of the target children and is named with the alias of the child and their age at the given session in form YMMDD. Transcription rules and other details can be found on the homepage coczefla.ff.cuni.cz.
A new version of the previously published corpus Chroma wih morphological annotation. The version 2023.07 differs from 2023.04 in that it includes all seven children and it went through an additional careful check of consistency and conformity to the CHAT transcription principles.
Two transcripts (Julie20221, Klara30424) from the previous versions (2022.07, 2019.07) were removed since they did not meet our criteria on dialogical format. All transcripts of recordings made during one day were split into one file. Thus, version 2023.07 consists of 183 files/transcripts. The number of utterances and tokens given here in LINDAT corresponds to children's lines only.
Files are in plain text with UTF-8 encoding. Each file represents one recording session of one of the target children and is named with the alias of the child and their age at the given session in form YMMDD. Transcription rules and other details can be found on the homepage coczefla.ff.cuni.cz.
The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 39 appeals to the public to donate blood in preparation for the expected military conflict. It includes illustrative shots of how donated blood is preserved for use in the combat environment. The report includes information about different blood groups and how healthy blood donors are tested by the Czechoslovak Red Cross.
Segment of the Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) 1942 No. 41 captures the funeral of Alexandr Commichau, head of the Reich Labour Service, held on 5 October 1942 in the Spanish Hall of Prague Castle, decorated with Nazi emblems for the occassion. The deceased´s military honours are on display next to a bier with the coffin. Mourners include the widow, a little girl and State Secretary Hermann Frank. Acting Reich Protector Kurt Daluege lays down a wreath from Adolf Hitler. The funeral speech is delivered by Deputy Chief General of RAD Wilhelm Decker (silent). The procession with the coffin, which will be transported to a crematorium in Prague, moves through the Matthias Gate.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 49AB from 1943 captures the concert called Five Years Leading the Nation, which was organised by the Board of Trustees for the Education of Youth to mark the fifth anniversary of Emil Hácha´s presidency, and held at Smetana Hall in the Municipal House in Prague on 29 November. General Secretary of the Board František Teuner gave a speech at the formal event. The programme included a selection of folk songs by Otakar Jeremiáš performed by the Czech Choir and the Kühn Children´s Choir accompanied by the Czech Philharmonic under the baton of Karel Šejna.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 42A from 1944 was shot during a concert organised by the Board of Trustees for the Education of Youth and held in the Smetana Hall of the Municipal House in Prague on 3 October. The concert was dedicated to mark the 70th birth anniversary of the late composer Josef Suk. The programme, prepared by the Czech Philharmonic led by conductor Otakar Pařík, included the symphonic poem "Praga".
Czech data - both train and test+eval sets, as well as the valency dictionary - for the CoNLL 2009 Shared Task. Documentation is included. The data are generated from PDT 2.0. LDC catalog number: LDC2009E34B and MSM 0021620838 (http://ufal.mff.cuni.cz:8080/bib/?section=grant&id=116488695895567&mode=view)
Czech trial (example) data for CoNLL 2009 Shared Task. The data are generated from PDT 2.0. LDC2009E32B and MSM 0021620838 (http://ufal.mff.cuni.cz:8080/bib/?section=grant&id=116488695895567&mode=view)
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.
This is a Czech Named Entity Corpus 1.0 transformed into the CoNLL format. The original corpus can be downloaded from: http://hdl.handle.net/11858/00-097C-0000-0023-1B04-C. The CoNLL transformation is described in this publication: https://link.springer.com/chapter/10.1007/978-3-642-40585-3_20.
This is a Czech Named Entity Corpus 2.0 transformed into the CoNLL format. The original corpus can be downloaded from: http://hdl.handle.net/11858/00-097C-0000-0023-1B22-8. The CoNLL transformation is described in this publication: https://link.springer.com/chapter/10.1007/978-3-642-40585-3_20.
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 0.1 consists of 17 datasets for 11 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 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), 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.
References to original resources whose harmonized versions are contained in the public edition of CorefUD 0.1:
- Catalan-AnCora:
Recasens, M. and Martí, M. A. (2010). AnCora-CO: Coreferentially Annotated Corpora for Spanish and Catalan. Language Resources and Evaluation, 44(4):315–345
- Czech-PCEDT:
Nedoluzhko, A., Novák, M., Cinková, S., Mikulová, M., and Mírovský, J. (2016). Coreference in Prague Czech-English Dependency Treebank. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 169–176, Portorož, Slovenia. European Language Resources Association.
- Czech-PDT:
Hajič, J., Bejček, E., Hlaváčová, J., Mikulová, M., Straka, M., Štěpánek, J., and Štěpánková, B. (2020). Prague Dependency Treebank - Consolidated 1.0. In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020), pages 5208–5218, Marseille, France. European Language Resources Association.
- English-GUM:
Zeldes, A. (2017). The GUM Corpus: Creating Multilayer Resources in the Classroom. Language Resources and Evaluation, 51(3):581–612.
- English-ParCorFull:
Lapshinova-Koltunski, E., Hardmeier, C., and Krielke, P. (2018). ParCorFull: a Parallel Corpus Annotated with Full Coreference. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association.
- French-Democrat:
Landragin, F. (2016). Description, modélisation et détection automatique des chaı̂nes de référence (DEMOCRAT). Bulletin de l’Association Française pour l’Intelligence Artificielle, (92):11–15.
- German-ParCorFull:
Lapshinova-Koltunski, E., Hardmeier, C., and Krielke, P. (2018). ParCorFull: a Parallel Corpus Annotated with Full Coreference. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association
- German-PotsdamCC:
Bourgonje, P. and Stede, M. (2020). The Potsdam Commentary Corpus 2.2: Extending annotations for shallow discourse parsing. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1061–1066, Marseille, France. European Language Resources Association.
- Hungarian-SzegedKoref:
Vincze, V., Hegedűs, K., Sliz-Nagy, A., and Farkas, R. (2018). SzegedKoref: A Hungarian Coreference Corpus. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association.
- Lithuanian-LCC:
Žitkus, V. and Butkienė, R. (2018). Coreference Annotation Scheme and Corpus for Lithuanian Language. In Fifth International Conference on Social Networks Analysis, Management and Security, SNAMS 2018, Valencia, Spain, October 15-18, 2018, pages 243–250. IEEE.
- Polish-PCC:
Ogrodniczuk, M., Glowińska, K., Kopeć, M., Savary, A., and Zawisławska, M. (2013). Polish coreference corpus. In Human Language Technology. Challenges for Computer Science and Linguistics - 6th Language and Technology Conference, LTC 2013, Poznań, Poland, December 7-9, 2013. Revised Selected Papers, volume 9561 of Lecture Notes in Computer Science, pages 215–226. Springer.
- Russian-RuCor:
Toldova, S., Roytberg, A., Ladygina, A. A., Vasilyeva, M. D., Azerkovich, I. L., Kurzukov,M., Sim, G., Gorshkov, D. V., Ivanova, A., Nedoluzhko, A., and Grishina, Y. (2014). Evaluating Anaphora and Coreference Resolution for Russian. In Komp’juternaja lingvistika i intellektual’nye tehnologii. Po materialam ezhegodnoj Mezhdunarodnoj konferencii
Dialog, pages 681–695.
- Spanish-AnCora:
Recasens, M. and Martí, M. A. (2010). AnCora-CO: Coreferentially Annotated Corpora for Spanish and Catalan. Language Resources and Evaluation, 44(4):315–345
References to original resources whose harmonized versions are contained in the ÚFAL-internal edition of CorefUD 0.1:
- Dutch-COREA:
Hendrickx, I., Bouma, G., Coppens, F., Daelemans, W., Hoste, V., Kloosterman, G., Mineur, A.-M., Van Der Vloet, J., and Verschelde, J.-L. (2008). A coreference corpus and resolution system for Dutch. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco. European Language Resources Association.
- English-ARRAU:
Uryupina, O., Artstein, R., Bristot, A., Cavicchio, F., Delogu, F., Rodriguez, K. J., and Poesio, M. (2020). Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus. Natural Language Engineering, 26(1):95–128.
- English-OntoNotes:
Weischedel, R., Hovy, E., Marcus, M., Palmer, M., Belvin, R., Pradhan, S., Ramshaw, L., and Xue, N. (2011). Ontonotes: A large training corpus for enhanced processing. In Handbook of Natural Language Processing and Machine Translation: DARPA Global Autonomous Language Exploitation, pages 54–63, New York. Springer-Verlag.
- English-PCEDT:
Nedoluzhko, A., Novák, M., Cinková, S., Mikulová, M., and Mírovský, J. (2016). Coreference in Prague Czech-English Dependency Treebank. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 169–176, Portorož, Slovenia. European Language Resources Association.
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 0.2 consists of 17 datasets for 11 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 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), 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.
Version 0.2 consists of exactly the same datasets as the version 0.1. All automatically parsed datasets were re-parsed for v0.2 using UDPipe 2 with models trained on UD 2.6. Catalan-AnCora, Spanish-AnCora and English-GUM have been updated to match the their UD 2.9 versions.
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 1.0 consists of 17 datasets for 11 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 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), 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. Version 1.0 consists of the same corpora and languages as the previous version 0.2; however, the English GUM dataset has been updated to a newer and larger version, and in the Czech/English PCEDT dataset, the train-dev-test split has been changed to be compatible with OntoNotes. Nevertheless, the main change is in the file format (the MISC attributes have new form and interpretation).
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 1.1 consists of 21 datasets for 13 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 17 datasets for 12 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 2 for Hungarian, 1 for Lithuanian, 2 for Norwegian, 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 1.0, the version 1.1 comprises new languages and corpora, namely Hungarian-KorKor, Norwegian-BokmaalNARC, Norwegian-NynorskNARC, and Turkish-ITCC. In addition, the English GUM dataset has been updated to a newer and larger version, and the conversion pipelines for most datasets have been refined (a list of all changes in each dataset can be found in the corresponding README file).
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).
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).
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.
In NLP Centre, dividing text into sentences is currently done with
a tool which uses rule-based system. In order to make enough training
data for machine learning, annotators manually split the corpus of contemporary text
CBB.blog (1 million tokens) into sentences.
Each file contains one hundredth of the whole corpus and all data were
processed in parallel by two annotators.
The corpus was created from ten contemporary blogs:
hintzu.otaku.cz
modnipeklo.cz
bloc.cz
aleneprokopova.blogspot.com
blog.aktualne.cz
fuchsova.blog.onaidnes.cz
havlik.blog.idnes.cz
blog.aktualne.centrum.cz
klusak.blogspot.cz
myego.cz/welldone
The corpus contains speech data of 2 Czech native speakers, male and female. The speech is very precisely articulated up to hyper-articulated, and the speech rate is low. The speech data with a highlighted articulation is suitable for teaching foreigners the Czech language, and it can also be used for people with hearing or speech impairment. The recorded sentences can be used either directly, e.g., as a part of educational material, or as source data for building complex educational systems incorporating speech synthesis technology. All recorded sentences were precisely orthographically annotated and phonetically segmented, i.e., split into phones, using modern neural network-based methods.
COSTRA 1.0 is a dataset of Czech complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing.
The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation.
The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting “skeleton” in the sentence embedding space.
Costra 1.1 is a new dataset for testing geometric properties of sentence embeddings spaces. In particular, it concentrates on examining how well sentence embeddings capture complex phenomena such paraphrases, tense or generalization. The dataset is a direct expansion of Costra 1.0, which was extended with more sentences and sentence comparisons.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 9B from 1943 captures an ice sports course for mandatory youth service instructors, which was organised by the Board of Trustees for the Education of Youth as part of the Ice Sports Week event held at Štvanice Ice Arena in Prague from 1 to 6 February. Training in speed skating and ice hockey was led by hockey players Josef Maleček, Vladimír Zábrodský and Jiří Tožička. The event was attended by General Secretary of the Board František Teuner.
CsEnVi Pairwise Parallel Corpora consist of Vietnamese-Czech parallel corpus and Vietnamese-English parallel corpus. The corpora were assembled from the following sources:
- OPUS, the open parallel corpus is a growing multilingual corpus of translated open source documents.
The majority of Vi-En and Vi-Cs bitexts are subtitles from movies and television series.
The nature of the bitexts are paraphrasing of each other's meaning, rather than translations.
- TED talks, a collection of short talks on various topics, given primarily in English, transcribed and with transcripts translated to other languages. In our corpus, we use 1198 talks which had English and Vietnamese transcripts available and 784 talks which had Czech and Vietnamese transcripts available in January 2015.
The size of the original corpora collected from OPUS and TED talks is as follows:
CS/VI EN/VI
Sentence 1337199/1337199 2035624/2035624
Word 9128897/12073975 16638364/17565580
Unique word 224416/68237 91905/78333
We improve the quality of the corpora in two steps: normalizing and filtering.
In the normalizing step, the corpora are cleaned based on the general format of subtitles and transcripts. For instance, sequences of dots indicate explicit continuation of subtitles across multiple time frames. The sequences of dots are distributed differently in the source and the target side. Removing the sequence of dots, along with a number of other normalization rules, improves the quality of the alignment significantly.
In the filtering step, we adapt the CzEng filtering tool [1] to filter out bad sentence pairs.
The size of cleaned corpora as published is as follows:
CS/VI EN/VI
Sentence 1091058/1091058 1113177/1091058
Word 6718184/7646701 8518711/8140876
Unique word 195446/59737 69513/58286
The corpora are used as training data in [2].
References:
[1] Ondřej Bojar, Zdeněk Žabokrtský, et al. 2012. The Joy of Parallelism with CzEng 1.0. Proceedings of LREC2012. ELRA. Istanbul, Turkey.
[2] Duc Tam Hoang and Ondřej Bojar, The Prague Bulletin of Mathematical Linguistics. Volume 104, Issue 1, Pages 75–86, ISSN 1804-0462. 9/2015
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)
Web corpus of Czech, created in 2011. Contains newspapers+magazines, discussions, blogs. See http://www.lrec-conf.org/proceedings/lrec2012/summaries/120.html for details. and GA405/09/0278
This is a document-aligned parallel corpus of English and Czech abstracts of scientific papers published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague, as reported in the institute's system Biblio. For each publication, the authors are obliged to provide both the original abstract in Czech or English, and its translation into English or Czech, respectively. No filtering was performed, except for removing entries missing the Czech or English abstract, and replacing newline and tabulator characters by spaces.
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.
The database contains annotated reflective sentences, which fall into the categories of reflective writing according to Ullmann's (2019) model. The dataset is ready to replicate these categories' prediction using machine learning. Available from: https://anonymous.4open.science/repository/c856595c-dfc2-48d7-aa3d-0ccc2648c4dc/data
This is the Czech Court Decisions Corpus (CzCDC 1.0). This corpus contains whole texts of the decisions from three top-tier courts (Supreme, Supreme Administrative and Constitutional court) in Czech republic. Court decisions are published from 1st January 1993 to 30th September 2018.
The language of decisions is Czech. Content of decisions is unedited and obtained directly from the competent court.
Decisions are in .txt format in three folders divided by courts.
Corpus contains three .csv files containing the list of all decisions with four columns:
- name of the file: exact file name of a decision with extension .txt;
- decision identifier (docket number): official identification of the decision as issued by the court;
- date of decision: in ISO 8601 (YYYY-MM-DD);
- court abbreviation: SupCo for Supreme Court, SupAdmCo for Supreme Administrative Court, ConCo for Constitutional Court
Statistics:
- SupCo: 111 977 decisions, 23 699 639 lines, 224 061 129 words, 1 462 948 200 bits;
- SupAdmCo: 52 660 decisions, 18 069 993 lines, 137 839 985 words, 1 067 826 507 bits;
- ConCo: 73 086 decisions, 6 178 371 lines, 98 623 753 words, 664 657 755 bits
- all courts combined: 237 723 decisions, 47 948 003 lines, 460 524 867 words, 3 195 432 462 bits
We present the Czech Court Decisions Dataset (CCDD) -- a dataset of 300 manually annotated court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.
AGREE is a dataset and task for evaluation of language models based on grammar agreement in Czech. The dataset consists of sentences with marked suffixes of past tense verbs. The task is to choose the right verb suffix which depends on gender, number and animacy of subject. It is challenging for language models because 1) Czech is morphologically rich, 2) it has relatively free word order, 3) high out-of-vocabulary (OOV) ratio, 4) predicate and subject can be far from each other, 5) subjects can be unexpressed and 6) various semantic rules may apply. The task provides a straightforward and easily reproducible way of evaluating language models on a morphologically rich language.
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.