Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 29A from 1944 was shot during the Week of Czech Youth event organised by the Board of Trustees for the Education of Youth and held from 1 to 9 July. The programme included a concert held on Old Town Square on 8 July. The orchestra and choir consisted of several hundred young musicians and singers. Minister of Education and People´s Enlightenment and Chairman of the Board Emanuel Moravec and Deputy Mayor Joseph Pfitzner watched the event from the balcony of the Old Town Hall. The Board of Trustees´ youth set out from the square in a parade through the streets of Prague. The following day, a sports afternoon took place at Strahov Stadium. Guests of honour included Prime Minister Jaroslav Krejčí and the General Secretary of the Board František Teuner. Emanuel Moravec spoke to the participants. The programme included women´s floor exercises, track and field races and women in stylised costumes dancing to folk songs. The event was concluded with the athletes and audience paying homage to Adolf Hitler.
The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 8A shows a speech delivered in Czech by Wenzel Jaksch, the MP for the German Social Democratic Workers' Party (DSAP), about the possible coexistence of, and understanding between, Czechs and Germans.
Trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
WikiSpeech is a content management system for the web-based creation of speech databases for the development of spoken language technology and basic research. Its main features are full support for the typical recording, annotation and project administration workflow, easy editing of the speech content, plus a fully localizable user interface. For the creation of a new speech database, it is only necessary to open a new project within WikiSpeech, provide a link to any static project information pages and upload the prompt material to be presented to the speakers. Recordings and annotation are performed via the WWW in a platform independent manner on any Java compatible computer. WikiSpeech currently has been localized to four languages: German, English, Romanian and Russian.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 9B from 1944 covered one of the events of the mandatory youth service organised by the Board of Trustees for the Education of Youth. Specifically, it consisted of recreational winter sports activities (skiing, sleighing) with the aim of improving physical fitness and strengthening collective spirit.
Wmatrix is a corpus comparison and annotation tool. It is web based and incorporates the CLAWS POS tagger and the USAS semantic tagger for English. It also generates frequency lists, concordances, key words and key semantic domains by comparative frequency profiling.
We provide the Vietnamese version of the multi-lingual test set from WMT 2013 [1] competition. The Vietnamese version was manually translated from English. For completeness, this record contains the 3000 sentences in all the WMT 2013 original languages (Czech, English, French, German, Russian and Spanish), extended with our Vietnamese version. Test set is used in [2] to evaluate translation between Czech, English and Vietnamese.
References
1. http://www.statmt.org/wmt13/evaluation-task.html
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
Testing set from WMT 2011 [1] competition, manually translated from Czech and English into Slovak. Test set contains 3003 sentences in Czech, Slovak and English. Test set is described in [2].
References:
[1] http://www.statmt.org/wmt11/evaluation-task.html
[2] Petra Galuščáková and Ondřej Bojar. Improving SMT by Using Parallel Data of a Closely Related Language. In Human Language Technologies - The Baltic Perspective - Proceedings of the Fifth International Conference Baltic HLT 2012, volume 247 of Frontiers in AI and Applications, pages 58-65, Amsterdam, Netherlands, October 2012. IOS Press. and The work on this project was supported by the grant EuroMatrixPlus (FP7-ICT-
2007-3-231720 of the EU and 7E09003 of the Czech Republic)
Training, development and text data (the same used for the Sentence-level Quality Estimation task) consist in English-German triplets (source, target and post-edit) belonging to the IT domain and already tokenized.
Training and development respectively contain 12,000 and 1,000 triplets, while the test set 2,000 instances. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training, development and test data consist in German sentences belonging to the IT domain and already tokenized. These sentences are the references of the data released for the 2016 edition of the WMT APE shared task. Differently from the data previously released, these sentences are obtained by manually translating the source sentence without leveraging the raw mt outputs. Training and development respectively contain 12,000 and 1,000 segments, while the test set 2,000 items. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training and development data for the WMT16 QE task. Test data will be published as a separate item.
This shared task will build on its previous four editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, sentence-level and document-level estimation. The sentence and word-level tasks will explore a large dataset produced from post-editions by professional translators (as opposed to crowdsourced translations as in the previous year). For the first time, the data will be domain-specific (IT domain). The document-level task will use, for the first time, entire documents, which have been human annotated for quality indirectly in two ways: through reading comprehension tests and through a two-stage post-editing exercise. Our tasks have the following goals:
- To advance work on sentence and word-level quality estimation by providing domain-specific, larger and professionally annotated datasets.
- To study the utility of detailed information logged during post-editing (time, keystrokes, actual edits) for different levels of prediction.
- To analyse the effectiveness of different types of quality labels provided by humans for longer texts in document-level prediction.
This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for the sentence and word-level tasks, and multiple MT systems were used to produce translations for the document-level task. Therefore, MT system-dependent information will be made available where possible.
The item contains models to tune for the WMT16 Tuning shared task for Czech-to-English.
CzEng 1.6pre (http://ufal.mff.cuni.cz/czeng/czeng16pre) corpus is used for the training of the translation models. The data is tokenized (using Moses tokenizer), lowercased and sentences longer than 60 words and shorter than 4 words are removed before training. Alignment is done using fast_align (https://github.com/clab/fast_align) and the standard Moses pipeline is used for training.
Two 5-gram language models are trained using KenLM: one only using the CzEng English data and the other is trained using all available English mono data for WMT except Common Crawl.
Also included are two lexicalized bidirectional reordering models, word based and hierarchical, with msd conditioned on both source and target of processed CzEng.
This item contains models to tune for the WMT16 Tuning shared task for English-to-Czech.
CzEng 1.6pre (http://ufal.mff.cuni.cz/czeng/czeng16pre) corpus is used for the training of the translation models. The data is tokenized (using Moses tokenizer), lowercased and sentences longer than 60 words and shorter than 4 words are removed before training. Alignment is done using fast_align (https://github.com/clab/fast_align) and the standard Moses pipeline is used for training.
Two 5-gram language models are trained using KenLM: one only using the CzEng Czech data and the other is trained using all available Czech mono data for WMT except Common Crawl.
Also included are two lexicalized bidirectional reordering models, word based and hierarchical, with msd conditioned on both source and target of processed CzEng.
Training and development data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in German-English triplets (source, target and post-edit) belonging to the pharmacological domain and already tokenized. Training and development respectively contain 25,000 and 1,000 triplets. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in 11,000 English-German triplets (source, target and post-edit) belonging to the IT domain and already tokenized. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training and development data for the WMT17 QE task. Test data will be published as a separate item.
This shared task will build on its previous five editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks will make use of a large dataset produced from post-editions by professional translators. The data will be domain-specific (IT and Pharmaceutical domains) and substantially larger than in previous years. In addition to advancing the state of the art at all prediction levels, our goals include:
- To test the effectiveness of larger (domain-specific and professionally annotated) datasets. We will do so by increasing the size of one of last year's training sets.
- To study the effect of language direction and domain. We will do so by providing two datasets created in similar ways, but for different domains and language directions.
- To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes.
Test data for the WMT17 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-1974
This shared task will build on its previous five editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks will make use of a large dataset produced from post-editions by professional translators. The data will be domain-specific (IT and Pharmaceutical domains) and substantially larger than in previous years. In addition to advancing the state of the art at all prediction levels, our goals include:
- To test the effectiveness of larger (domain-specific and professionally annotated) datasets. We will do so by increasing the size of one of last year's training sets.
- To study the effect of language direction and domain. We will do so by providing two datasets created in similar ways, but for different domains and language directions.
- To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes.
Training and development data for the WMT 2018 Automatic post-editing task. They consist in English-German triplets (source, target and post-edit) belonging to the information technology domain and already tokenized. Training and development respectively contain 13,442 and 1,000 triplets. A neural machine translation system has been used to generate the target segments. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Test data for the WMT18 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-2619.
This shared task will build on its previous six editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks make use of datasets produced from post-editions by professional translators. The datasets are domain-specific (IT and life sciences/pharma domains) and extend from those used previous years with more instances and more languages. One important addition is that this year we also include datasets with neural MT outputs. In addition to advancing the state of the art at all prediction levels, our specific goals are:
To study the performance of quality estimation approaches on the output of neural MT systems. We will do so by providing datasets for two language language pairs where the same source segments are translated by both a statistical phrase-based and a neural MT system.
To study the predictability of deleted words, i.e. words that are missing in the MT output. TO do so, for the first time we provide data annotated for such errors at training time.
To study the effectiveness of explicitly assigned labels for phrases. We will do so by providing a dataset where each phrase in the output of a phrase-based statistical MT system was annotated by human translators.
To study the effect of different language pairs. We will do so by providing datasets created in similar ways for four language language pairs.
To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
Measure progress over years at all prediction levels. We will do so by using last year's test set for comparative experiments.
In-house statistical and neural MT systems were built to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes. Participants are allowed to explore any additional data and resources deemed relevant.
Training and development data for the WMT18 QE task. Test data will be published as a separate item.
This shared task will build on its previous six editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks make use of datasets produced from post-editions by professional translators. The datasets are domain-specific (IT and life sciences/pharma domains) and extend from those used previous years with more instances and more languages. One important addition is that this year we also include datasets with neural MT outputs. In addition to advancing the state of the art at all prediction levels, our specific goals are:
To study the performance of quality estimation approaches on the output of neural MT systems. We will do so by providing datasets for two language language pairs where the same source segments are translated by both a statistical phrase-based and a neural MT system.
To study the predictability of deleted words, i.e. words that are missing in the MT output. TO do so, for the first time we provide data annotated for such errors at training time.
To study the effectiveness of explicitly assigned labels for phrases. We will do so by providing a dataset where each phrase in the output of a phrase-based statistical MT system was annotated by human translators.
To study the effect of different language pairs. We will do so by providing datasets created in similar ways for four language language pairs.
To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
Measure progress over years at all prediction levels. We will do so by using last year's test set for comparative experiments.
In-house statistical and neural MT systems were built to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes. Participants are allowed to explore any additional data and resources deemed relevant.
Marian NMT model for Catalan to Occitan translation. It is a multi-task model, producing also a phonemic transcription of the Catalan source. The model was submitted to WMT'21 Shared Task on Multilingual Low-Resource Translation for Indo-European Languages as a CUNI-Contrastive system for Catalan to Occitan.
Marian NMT model for Catalan to Occitan translation. Primary CUNI submission for WMT21 Multilingual
Low-Resource Translation for Indo-European Languages Shared Task.
Marian multilingual translation model from Catalan into Romanian, Italian and Occitan. Primary CUNI submission for WMT21 Multilingual
Low-Resource Translation for Indo-European Languages Shared Task.
Dictionaries with different representations for various languages. Representations include brown clusters of different sizes and morphological dictionaries extracted using different morphological analyzers. All representations cover the most frequent 250,000 word types on the Wikipedia version of the respective language.
Analzers used: MAGYARLANC (Hungarian, Zsibrita et al. (2013)), FREELING (English and Spanish, Padro and Stanilovsky (2012)), SMOR (German, Schmid et al. (2004)), an MA from Charles University (Czech, Hajic (2001)) and LATMOR (Latin, Springmann et al. (2014)).
German has various homophonous sibilant fricatives of phonemic or morphemic nature that can appear in word-final position. In English, the functional status of a word-final \s\ influences its durational properties, with phonemic \s\ being longer than morphemic types. The data set presented here is a small selection of laboratory-elicited German sentences containing various words with final sibilant phonemes (e.g., "das Haus") and morphemes (plural, genitive, clitic, inflection). Durations of the \s\ types were measured and compared across the conditions. An ANOVA between the \s\ types and post-hoc Tukey pair-wise comparisons are presented that show various significant differences.
The submission consists of a csv data file, containing a number of variables, and a PDF document detailing the experiment and variables.
Czech translation of WordSim353. The Czech translation of English WordSim353 word pairs were obtained from four translators. All translation variants were scored according to the lexical similarity/relatedness annotation instructions for WordSim353 annotators, by 25 Czech annotators. The resulting data set consists of two annotation files: "WordSim353-cs.csv" and "WordSim-cs-Multi.csv". Both files are encoded in UTF-8, have a header, text is enclosed in double quotes, and columns are separated by commas. The rows are numbered. The WordSim-cs-Multi data set has rows numbered from 1 to 634, whereas the row indices in the WordSim353-cs data set reflect the corresponding row numbers in the WordSim-cs-Multi data set.
The WordSim353-cs file contains a one-to-one mapping selection of 353 Czech equivalent pairs whose judgments have proven to be most similar to the judgments of their corresponding English originals (compared by the absolute value of the difference between the means over all annotators in each language counterpart). In one case ("psychology-cognition"), two Czech equivalent pairs had identical means as well as confidence intervals, so we randomly selected one.
The "WordSim-cs-Multi.csv" file contains human judgments for all translation variants.
In both data sets, we preserved all 25 individual scores. In the WordSim353-cs data set, we added a column with their Czech means as well as a column containing the original English means and 95% confidence intervals in separate columns for each mean (computed by the CI function in the Rmisc R package). The WordSim-cs-Multi data set contains only the Czech means and confidence intervals. For the most convenient lexical search, we provided separate columns with the respective Czech and English single words, entire word pairs, and eventually an English-Czech quadruple in both data sets.
The data set also contains an xls table with the four translations and a preliminary selection of the best variants performed by an adjudicator.
Segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1942, issue no. 32B, reports on a workers´ holiday organized by the Reinhard Heydrich Foundation for Workers´ Recuperation in Český Šternberk. A view of the exterior of the health resort. Holidaymakers are sunbathing on the terrace. A waiter is carrying plates full of food in the dining room. People are eating. A close-up of a man drinking beer from a beer mug. Holidaymakers playing volleyball. A fisherman is sitting on the bank of the Sázava River. People are bathing in the river and in the weir. Český Šternberk Castle can be seen in the background.
Segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1942, issue no. 24A, reports on a workers´ holiday organized by the Reinhard Heydrich Foundation for Workers´ Recuperation in Luhačovice. Footage of a train arriving at the railway station and the welcoming of the holidaymakers. Lunch is ready for visitors at a local restaurant. Holidaymakers rest on the hotel terrace, some play volleyball or skittles. Others explore the surrounding countryside. Footage of a walk to the Luhačovice Dam. Girls sit on the grass, weaving flower wreaths. Holidaymakers taste the local mineral water.
Segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1942, issue no. 32A, reports on a workers´ holiday organized by the Reinhard Heydrich Foundation for Workers´ Recuperation in the village of Věšín u Blatné. Holidaymakers walk through the health resort´s gate. Morning exercise in the courtyard. A waiter carries plates full of food across the outdoor dining room, people are eating. Footage of holidaymakers enjoying leisure activities, an improvised boxing match, swimming in the pool, playing water sports. A view of an entrance arch with a sign saying "Welcome to the Workers´ Health Resort".
A dictionary of reconstructed Proto-Germanic, organized by reconstructed lemmata, with each entry including the attested reflexes in the daughter Germanic languages, as well as cognates in the other Indo-European branches.
Angabe von Wort, Anzahl, Häufigkeitsklasse, Beschreibung, Sachgebiet, Morphologie, Relationen zu anderen Wörtern (z. B. Synonymie), Links zu anderen Wörtern, Dornseiff-Bedeutungsgruppen, Beispielen (u.a. entnommen aus spiegel.de, sueddeutsche.de), signifikanten Kookkurenzen, signifikanten linken und rechten Nachbarn
Tool for designing and performing Word Sense Disambiguation (WSD) experiments. Current version (prototype) facilitates the construction and evaluation of WSD methods in the supervised Machine Learning paradigm.
Xaira is the current name for a new version of SARA, the text searching software originally developed at OUCS for use with the British National Corpus. This new version has been entirely re-written as a general purpose XML search engine, which will operate on any corpus of well-formed XML documents. It is however best used with TEI-conformant documents.
XSH is a powerfull command-line tool for querying, processing and editing XML documents. It features a shell-like interface with auto-completion for comfortable interactive work, but can be as well used for off-line (batch) processing of XML data.
YAWA is a four stage lexical aligner that uses bilingual translation lexicons produced by [[http://www.clarin.eu/tools/translation-equivalents-extractor|TREQ]] and phrase boundaries detection to align words of a given bitext. Using this alignment, in stage 2 a language dependent module takes over and produces alignments of the remaining lexical tokens within aligned chunks. Stage 3 is specialized in aligning blocks of consecutive unaligned tokens and stage 4 deletes alignments that are likely to be wrong.
Developed in PERL, YAWA is language independent, except for the modules that realise alignments specific to the pairs of aligned languages. So far, it works just for Ro-En pair of languages. It requires a parallel corpus in [[http://www.xces.org|XCES]] format, morpho-syntactically annotated and lemmatized (using [[http://www.clarin.eu/tools/ttl-tokenizing-tagging-and-lemmatizing-free-running-texts|TTL]]), and translation dictionaries produced by [[http://www.clarin.eu/tools/translation-equivalents-extractor|TREQ]].
YAWA’s individual F-measure is 81.22%. Currently YAWA is a part of the [[http://www.clarin.eu/tools/cowal-combined-word-aligner|COWAL]] combined lexical alignment platform.
More detailed descriptions are available in [[http://www.racai.ro/~tufis/papers|the following papers]]:
-- Radu Ion (2007). Word Sense Disambiguation Methods Applied to English and Romanian. (in Romanian). PhD thesis. Romanian Academy, Bucharest
-- Dan Tufiş (2007). Exploiting Aligned Parallel Corpora in Multilingual Studies and Applications. In Toru Ishida, Susan R. Fussell, and Piek T.J.M. Vossen (eds.), Intercultural Collaboration. First International Workshop (IWIC 2007), volume 4568 of Lecture Notes in Computer Science, pp. 103-117. Springer-Verlag, August 2007. ISBN 978-3-540-73999-9.
-- Dan Tufiş, Radu Ion, Alexandru Ceauşu, and Dan Ştefănescu (2006). Improved Lexical Alignment by Combining Multiple Reified Alignments. In Toru Ishida, Susan R. Fussell, and Piek T.J.M. Vossen (eds.), Proceedings of the 11th Conference EACL2006, pp. 153-160, Trento, Italy, April 2006. Association for Computational Linguistics. ISBN 1-9324-32-61-2.
A selection of poetic texts (71,490 words) from the Old English Section of the Helsinki Corpus of English Texts, syntactically and morphologically annotated.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 51B from 1943 depicts the Youth Basketball Championship organised by the Board of Trustees for the Education of Youth and held in the Great Hall of Lucerna Palace in Prague from 10 to 12 December. The boys´ final was won by the Central Bohemia I team, who beat the Brno Region I team 27:13. The girls´ final was won by the Brno Region I team, who beat the team from Polabí 17:5.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 7B from 1944 was shot at the Youth Ice Sports Championship, which culminated with the Ice Sports Week organised by the Board of Trustees for the Education of Youth at Štvanice Ice Arena in Prague from 1 to 6 February. The team LTC Prague beat the team SSC Říčany 4:0 to win the youth ice hockey final. General Secretary of the Board František Teuner presented diplomas to the winners of Ice Sports Week.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 7A from 1944 contains footage from the Youth Ice Sports Championship , which culminated with the Ice Sports Week organised by the Board of Trustees for the Education of Youth and held at Štvanice Ice Arena in Prague from 1 to 6 February. The programme included a performance of single figure skating.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 20A from 1944 was shot during the Youth Spring Day event organised by the Board of Trustees for the Education of Youth and held across the Protectorate on 7 May 1944. The purpose of the event was to renew folk customs and traditions. In Prague´s Stromovka, girls in folk costumes danced a quadrille dance called "Česká beseda" under a maypole. Boys celebrated the day with races.
Segment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 26A from 1944 was shot at the regional elimination swimming races organised by the Board of Trustees for the Education of Youth and held in Luleč near Vyškov on 18 June. The swimmer Kopřiva set a new record in the 100 m backstroke. The winners qualified for the Youth Swimming Championship held in Prague as part of the Board´s Week of Czech Youth.
The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1937, issue no. 52 reports on the visit of the French Minister of Foreign Affairs Yvon Delbos to Prague on 15 December 1937. Yvon Delbos is shown meeting with President Edvard Beneš, and also at a banquet in Czernin Palace with Prime Minister Milan Hodža, the Minister of Foreign Affairs Kamil Krofta, the Chairman of the Senate František Soukup, and the Chairman of the Parliament Jan Malypetr. The footage closes with the departure of Yvon Delbos from Wilson Station as he says good-bye to the people of Prague
Two photographs of actress Zdena Kavková. Kavková with her colleague Vladimír Slavínský in Děvče ze Stříbrné hranice (The Girl from the Silver Frontier, dir. Vladimír Slavínský, 1921).
Minister Zdeněk Nejedlý with his colleagues Zdeněk Fierlinger and Václav Kopecký on the platform during a First of May celebration. Nejedlý officially opens the Mirotice house where Mikoláš Aleš was born in a segment from Československé filmové noviny (Czechoslovak Film News) 1952, issue no. 46. On the occasion of his 75th birthday Professor Nejedlý accepts the Order of Lenin and an honorary diploma from the University of Moscow from Soviet Ambassador Aleksandr Efremovich Bogomolov in a segment from Československý filmový týdeník (Czechoslovak Film Weekly Newsreel) 1953, issue no. 9.
Actor Zdeněk Štěpánek at the Secondary School of Decorative Arts with his own glass portrait carved in glass in a segment from Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel) 1942, issue no. 28. Štěpánek in Preludium (Prelude, dir. František Čáp, 1941). Štěpánek in Josef Kajetán Tyl (dir. Svatopluk Innemann, 1925). Štěpánek with his colleague Vera Baranovská in Svatý Václav (St. Wenceslas, dir. Jan S. Kolár, 1929).
Actress Zdenka Gräfová with her colleague in a Brno theatre production in a fragmented segment from Československý filmový týdeník (Czechoslovak Film Weekly Newsreel) 1953, issue no. 5.
Corpus of the weekly Die Zeit from 1946 - present day (complete runs from 1996). Over 100 million words in 200,000 articles. Updated daily. Part of DWDS project.
Access to full texts (literary, historical, scientific, ... texts); Volltextbibliothek; keine Beschränkung auf literarische Texte (auch z.B. naturwissenschaftliche, geschichtliche, ... Texte)