Creator: Popel, Martin / Harvested from: LINDAT/CLARIAH-CZ repository

Start Over Creator Popel, Martin Harvested from LINDAT/CLARIAH-CZ repository

1. CoNLL 2017 Shared Task System Outputs

Creator:: Zeman, Daniel, Potthast, Martin, Straka, Milan, Popel, Martin, Dozat, Timothy, Qi, Peng, Manning, Christopher, Shi, Tianze, Wu, Felix G., Chen, Xilun, Cheng, Yao, Björkelund, Anders, Falenska, Agnieszka, Yu, Xiang, Kuhn, Jonas, Che, Wanxiang, Guo, Jiang, Wang, Yuxuan, Zheng, Bo, Zhao, Huaipeng, Liu, Yang, Teng, Dechuan, Liu, Ting, Lim, Kyungtae, Poibeau, Thierry, Sato, Motoki, Manabe, Hitoshi, Noji, Hiroshi, Matsumoto, Yuji, Kırnap, Ömer, Önder, Berkay Furkan, Yuret, Deniz, Straková, Jana, Vania, Clara, Zhang, Xingxing, Lopez, Adam, Heinecke, Johannes, Asadullah, Munshi, Kanerva, Jenna, Luotolahti, Juhani, Ginter, Filip, Kuan, Yu, Sofroniev, Pavel, Schill, Erik, Hinrichs, Erhard, Nguyen, Dat Quoc, Dras, Mark, Johnson, Mark, Qian, Xian, Vilares, David, Gómez-Rodríguez, Carlos, Aufrant, Lauriane, Wisniewski, Guillaume, Yvon, François, Dumitrescu, Stefan Daniel, Boroş, Tiberiu, Tufiş, Dan, Das, Ayan, Zaffar, Affan, Sarkar, Sudeshna, Wang, Hao, Zhao, Hai, Zhang, Zhisong, Hornby, Ryan, Taylor, Clark, Park, Jungyeul, de Lhoneux, Miryam, Shao, Yan, Basirat, Ali, Kiperwasser, Eliyahu, Stymne, Sara, Goldberg, Yoav, Nivre, Joakim, Akkuş, Burak Kerim, Azizoglu, Heval, Cakici, Ruket, Moor, Christophe, Merlo, Paola, Henderson, James, Wang, Haozhou, Ji, Tao, Wu, Yuanbin, Lan, Man, de la Clergerie, Eric, Sagot, Benoît, Seddah, Djamé, More, Amir, Tsarfaty, Reut, Kanayama, Hiroshi, Muraoka, Masayasu, Yoshikawa, Katsumasa, Garcia, Marcos, and Gamallo, Pablo
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: dependency parser and parsebank
Language:: Arabic, Bulgarian, Russia Buriat, Czech, Catalan, Church Slavic, Danish, German, Modern Greek (1453-), English, Spanish, Estonian, Basque, Persian, Finnish, French, Irish, Galician, Gothic, Ancient Greek (to 1453), Hebrew, Hindi, Croatian, Upper Sorbian, Hungarian, Indonesian, Italian, Japanese, Kazakh, Northern Kurdish, Korean, Latin, Latvian, Dutch, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Northern Sami, Swedish, Turkish, Uighur, Ukrainian, Urdu, Vietnamese, and Chinese
Description:: This package contains the system outputs from the CoNLL 2017 Shared Task in Multilingual Parsing from Raw Text to Universal Dependencies.
Rights:: Licence Universal Dependencies v2.0, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-UD-2.0, and PUB

2. Coreference in Universal Dependencies 0.1 (CorefUD 0.1)

Creator:: Nedoluzhko, Anna, Novák, Michal, Popel, Martin, Žabokrtský, Zdeněk, and Zeman, Daniel
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: dependency, treebank, coreference, bridging relations, and harmonized annotation
Language:: Catalan, Czech, Dutch, English, French, German, Hungarian, Lithuanian, Polish, Russian, and Spanish
Description:: 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.
Rights:: Licence CorefUD v0.1, https://lindat.mff.cuni.cz/repository/xmlui/page/license-corefud-0.1, and PUB

3. Coreference in Universal Dependencies 0.2 (CorefUD 0.2)

Creator:: Nedoluzhko, Anna, Novák, Michal, Popel, Martin, Žabokrtský, Zdeněk, and Zeman, Daniel
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: dependency, treebank, coreference, bridging relations, and harmonized annotation
Language:: Catalan, Czech, Dutch, English, French, German, Hungarian, Lithuanian, Polish, Russian, and Spanish
Description:: 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.
Rights:: Licence CorefUD v0.2, https://lindat.mff.cuni.cz/repository/xmlui/page/license-corefud-0.2, and PUB

4. Coreference in Universal Dependencies 1.0 (CorefUD 1.0)

Creator:: Nedoluzhko, Anna, Novák, Michal, Popel, Martin, Žabokrtský, Zdeněk, Zeldes, Amir, Zeman, Daniel, Bourgonje, Peter, Cinková, Silvie, Hajič, Jan, Hardmeier, Christian, Krielke, Pauline, Landragin, Frédéric, Lapshinova-Koltunski, Ekaterina, Martí, M. Antònia, Mikulová, Marie, Ogrodniczuk, Maciej, Recasens, Marta, Stede, Manfred, Straka, Milan, Toldova, Svetlana, Vincze, Veronika, and Žitkus, Voldemaras
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: dependency, treebank, coreference, bridging relations, and harmonized annotation
Language:: Catalan, Czech, Dutch, English, French, German, Hungarian, Lithuanian, Polish, Russian, and Spanish
Description:: 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).
Rights:: Licence CorefUD v0.2, https://lindat.mff.cuni.cz/repository/xmlui/page/license-corefud-0.2, and PUB

5. Coreference in Universal Dependencies 1.1 (CorefUD 1.1)

Creator:: Novák, Michal, Popel, Martin, Žabokrtský, Zdeněk, Zeman, Daniel, Nedoluzhko, Anna, Acar, Kutay, Bourgonje, Peter, Cinková, Silvie, Cebiroğlu Eryiğit, Gülşen, Hajič, Jan, Hardmeier, Christian, Haug, Dag, Jørgensen, Tollef, Kåsen, Andre, Krielke, Pauline, Landragin, Frédéric, Lapshinova-Koltunski, Ekaterina, Mæhlum, Petter, Martí, M. Antònia, Mikulová, Marie, Nøklestad, Anders, Ogrodniczuk, Maciej, Øvrelid, Lilja, Pamay Arslan, Tuğba, Recasens, Marta, Solberg, Per Erik, Stede, Manfred, Straka, Milan, Toldova, Svetlana, Vadász, Noémi, Velldal, Erik, Vincze, Veronika, Zeldes, Amir, and Žitkus, Voldemaras
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: dependency, treebank, coreference, bridging relations, and harmonized annotation
Language:: Catalan, Czech, English, French, German, Hungarian, Lithuanian, Norwegian, Polish, Russian, Spanish, and Turkish
Description:: 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).
Rights:: Licence CorefUD v1.1, https://lindat.mff.cuni.cz/repository/xmlui/page/license-corefud-1.1, and PUB

6. Coreference in Universal Dependencies 1.2 (CorefUD 1.2)

Creator:: Popel, Martin, Novák, Michal, Žabokrtský, Zdeněk, Zeman, Daniel, Nedoluzhko, Anna, Acar, Kutay, Bamman, David, Bourgonje, Peter, Cinková, Silvie, Eckhoff, Hanne, Cebiroğlu Eryiğit, Gülşen, Hajič, Jan, Hardmeier, Christian, Haug, Dag, Jørgensen, Tollef, Kåsen, Andre, Krielke, Pauline, Landragin, Frédéric, Lapshinova-Koltunski, Ekaterina, Mæhlum, Petter, Martí, M. Antònia, Mikulová, Marie, Nøklestad, Anders, Ogrodniczuk, Maciej, Øvrelid, Lilja, Pamay Arslan, Tuğba, Recasens, Marta, Solberg, Per Erik, Stede, Manfred, Straka, Milan, Swanson, Daniel, Toldova, Svetlana, Vadász, Noémi, Velldal, Erik, Vincze, Veronika, Zeldes, Amir, and Žitkus, Voldemaras
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: coreference, bridging relations, harmonized annotation, dependency, and treebank
Language:: Ancient Greek (to 1453), Ancient Hebrew, Catalan, Czech, English, French, German, Hungarian, Lithuanian, Norwegian, Church Slavic, Polish, Russian, Spanish, and Turkish
Description:: 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).
Rights:: Licence CorefUD v1.2, https://lindat.mff.cuni.cz/repository/xmlui/page/license-corefud-1.2, and PUB

7. CUBBITT Translation Models (en-cs) (v1.0)

Creator:: Popel, Martin, Tomková, Markéta, Tomek, Jakub, Kaiser, Łukasz, Uszkoreit, Jakob, Bojar, Ondřej, and Žabokrtský, Zdeněk
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: tool and toolService
Subject:: machine translation, neural machine translation, transformer, and cubbitt
Language:: English and Czech
Description:: 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)
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

8. CUBBITT Translation Models (en-fr) (v1.0)

Creator:: Popel, Martin, Tomková, Markéta, Tomek, Jakub, Kaiser, Łukasz, Uszkoreit, Jakob, Bojar, Ondřej, and Žabokrtský, Zdeněk
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: tool and toolService
Subject:: machine translation, neural machine translation, transformer, and cubbitt
Language:: English and French
Description:: CUBBITT En-Fr 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->fr: 38.2 fr->en: 36.7 (Evaluated using multeval: https://github.com/jhclark/multeval)
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

9. CUBBITT Translation Models (en-pl) (v1.0)

Creator:: Popel, Martin, Tomková, Markéta, Tomek, Jakub, Kaiser, Łukasz, Uszkoreit, Jakob, Bojar, Ondřej, and Žabokrtský, Zdeněk
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: tool and toolService
Subject:: machine translation, neural machine translation, transformer, and cubbitt
Language:: English and Polish
Description:: CUBBITT En-Pl 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 newstest2020 (BLEU): en->pl: 12.3 pl->en: 20.0 (Evaluated using multeval: https://github.com/jhclark/multeval)
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

10. Czech image captioning, machine translation, and sentiment analysis (Neural Monkey models)

Creator:: Libovický, Jindřich, Rosa, Rudolf, Helcl, Jindřich, and Popel, Martin
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: suiteOfTools and toolService
Subject:: sentiment analysis, machine translation, image captioning, neural networks, transformer, and Neural Monkey
Language:: Czech and English
Description:: 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!
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

11. Czech image captioning, machine translation, sentiment analysis and summarization (Neural Monkey models)

Creator:: Libovický, Jindřich, Rosa, Rudolf, Helcl, Jindřich, and Popel, Martin
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: suiteOfTools and toolService
Subject:: sentiment analysis, machine translation, image captioning, neural networks, transformer, Neural Monkey, and summarization
Language:: Czech and English
Description:: 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!
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

12. Czech-English Parallel Corpus 1.0 (CzEng 1.0)

Creator:: Bojar, Ondřej, Žabokrtský, Zdeněk, Dušek, Ondřej, Galuščáková, Petra, Majliš, Martin, Mareček, David, Maršík, Jiří, Novák, Michal, Popel, Martin, and Tamchyna, Aleš
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: corpus, parallel corpus, treebank, and alignment
Language:: Czech and English
Description:: CzEng 1.0 is the fourth release of a sentence-parallel Czech-English corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL) freely available for non-commercial research purposes. CzEng 1.0 contains 15 million parallel sentences (233 million English and 206 million Czech tokens) from seven different types of sources automatically annotated at surface and deep (a- and t-) layers of syntactic representation. and EuroMatrix Plus (FP7-ICT-2007-3-231720 of the EU and 7E09003+7E11051 of the Ministry of Education, Youth and Sports of the Czech Republic), Faust (FP7-ICT-2009-4-247762 of the EU and 7E11041 of the Ministry of Education, Youth and Sports of the Czech Republic), GAČR P406/10/P259, GAUK 116310, GAUK 4226/2011
Rights:: Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0), http://creativecommons.org/licenses/by-nc-sa/3.0/, and PUB

13. Europarl QTLeap WSD/NED corpus

Creator:: Agirre, Eneko, Branco, António, Popel, Martin, and Simov, Kiril
Publisher:: University of the Basque Country, UPV/EHU, Faculty of Science, Univeristy of Lisbon, FCUL, Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), and Bulgarian Academy of Sciences, IICT-BAS
Type:: text and corpus
Subject:: annotated corpus and multilingual
Language:: Basque, Bulgarian, Czech, English, Portuguese, and Spanish
Description:: This corpora is part of Deliverable 5.5 of the European Commission project QTLeap FP7-ICT-2013.4.1-610516 (http://qtleap.eu). The texts are sentences from the Europarl parallel corpus (Koehn, 2005). We selected the monolingual sentences from parallel corpora for the following pairs: Bulgarian-English, Czech-English, Portuguese-English and Spanish-English. The English corpus is comprised by the English side of the Spanish-English corpus. Basque is not in Europarl. In addition, it contains the Basque and English sides of the GNOME corpus. The texts have been automatically annotated with NLP tools, including Word Sense Disambiguation, Named Entity Disambiguation and Coreference resolution. Please check deliverable D5.6 in http://qtleap.eu/deliverables for more information.
Rights:: Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB

14. FAUST cs-en 0.5

Creator:: Hajič, Jan, Mareček, David, Fučíková, Eva, Cinková, Silvie, Štěpánek, Jan, Mikulová, Marie, and Popel, Martin
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: noisy texts, parallel corpus, and machine translation
Language:: English and Czech
Description:: This machine translation test set contains 2223 Czech sentences collected within the FAUST project (https://ufal.mff.cuni.cz/grants/faust, http://hdl.handle.net/11234/1-3308). Each original (noisy) sentence was normalized (clean1 and clean2) and translated to English independently by two translators.
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

15. HamleDT 2.0

Creator:: Zeman, Daniel, Mareček, David, Mašek, Jan, Popel, Martin, Ramasamy, Loganathan, Rosa, Rudolf, Štěpánek, Jan, and Žabokrtský, Zdeněk
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: treebank, Stanford dependencies, Prague dependencies, harmonization, common annotation style, and Interset
Language:: Arabic, Bulgarian, Bengali, Catalan, Czech, Danish, German, Modern Greek (1453-), English, Spanish, Estonian, Basque, Persian, Finnish, Ancient Greek (to 1453), Hindi, Hungarian, Italian, Japanese, Latin, Dutch, Portuguese, Romanian, Russian, Slovak, Slovenian, Swedish, Tamil, Telugu, and Turkish
Description:: HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular recently. We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes.
Rights:: HamleDT 2.0 Licence Agreement, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-hamledt-2.0, and ACA

16. HamleDT 3.0

Creator:: Zeman, Daniel, Mareček, David, Mašek, Jan, Popel, Martin, Ramasamy, Loganathan, Rosa, Rudolf, Štěpánek, Jan, and Žabokrtský, Zdeněk
Publisher:: Charles University
Type:: text and corpus
Subject:: annotated corpus, morphology, syntax, dependency, treebank, harmonized annotation, and common annotation style
Language:: Arabic, Basque, Bengali, Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Modern Greek (1453-), Ancient Greek (to 1453), Hebrew, Hindi, Hungarian, Indonesian, Irish, Italian, Japanese, Latin, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Tamil, Telugu, and Turkish
Description:: HamleDT (HArmonized Multi-LanguagE Dependency Treebank) is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. This version uses Universal Dependencies as the common annotation style. Update (November 1017): for a current collection of harmonized dependency treebanks, we recommend using the Universal Dependencies (UD). All of the corpora that are distributed in HamleDT in full are also part of the UD project; only some corpora from the Patch group (where HamleDT provides only the harmonizing scripts but not the full corpus data) are available in HamleDT but not in UD.
Rights:: HamleDT 3.0 License Terms, https://lindat.mff.cuni.cz/repository/xmlui/page/licence-hamledt-3.0, and PUB

17. LINDAT Translation service

Creator:: Košarko, Ondřej, Variš, Dušan, and Popel, Martin
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: service and toolService
Subject:: machine translation and frontend
Description:: Source code of the LINDAT Translation service frontend. The service provides a UI and a simple rest api that accesses machine translation models served by tensorflow serving. The most recent version of the code is available at https://github.com/ufal/lindat_translation.
Rights:: BSD 2-Clause "Simplified" or "FreeBSD" license, http://opensource.org/licenses/BSD-2-Clause, and PUB

18. LongEval Click-Model Relevance Judgements (Qrels)

Creator:: Galuščáková, Petra, Devaud, Romain, Gonzalez-Saez, Gabriela, Mulhem, Philippe, Goeuriot, Lorraine, Piroi, Florina, and Popel, Martin
Publisher:: Université Grenoble Alpes
Type:: text and corpus
Subject:: information retrieval, automatic evaluation, and evaluation
Language:: French and English
Description:: The collection comprises the relevance judgments used in the 2023 LongEval Information Retrieval Lab (https://clef-longeval.github.io/), organized at CLEF. It consists of three sets of relevance judgments: 1) Relevance judgments for the heldout queries from the LongEval Train Collection (http://hdl.handle.net/11234/1-5010). 2) Relevance judgments for the short-term persistence (sub-task A) queries from the LongEval Test Collection (http://hdl.handle.net/11234/1-5139). 3) Relevance judgments for the long-term persistence (sub-task B) queries from the LongEval Test Collection (http://hdl.handle.net/11234/1-5139). These judgments were provided by the Qwant search engine (https://www.qwant.com) and were generated using a click model. The click model output was based on the clicks of Qwant's users, but it mitigates noise from raw user clicks caused by positional bias and also better safeguards users' privacy. Consequently, it can serve as a reliable soft relevance estimate for evaluating and training models. The collection includes a total of 1,420 judgments for the heldout queries, with 74 considered highly relevant and 326 deemed relevant. For the short-term sub-task queries, there are 12,217 judgments, including 762 highly relevant and 2,608 relevant ones. As for the long-term sub-task queries, there are 13,467 judgments, with 936 being highly relevant and 2,899 relevant.
Rights:: Qwant LongEval Attribution-NonCommercial-ShareAlike License, https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License, and PUB

19. LongEval Test Collection

Creator:: Galuščáková, Petra, Devaud, Romain, Gonzalez-Saez, Gabriela, Mulhem, Philippe, Goeuriot, Lorraine, Piroi, Florina, and Popel, Martin
Publisher:: Université Grenoble Alpes, Qwant, Research Studios Austria, and Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: information retrieval, cross-language, cross-lingual information retrieval, parallel corpus, and search
Language:: French and English
Description:: The collection consists of queries and documents provided by the Qwant search Engine (https://www.qwant.com). The queries, which were issued by the users of Qwant, are based on the selected trending topics. The documents in the collection are the webpages which were selected with respect to these queries using the Qwant click model. Apart from the documents selected using this model, the collection also contains randomly selected documents from the Qwant index. The collection serves as the official test collection for the 2023 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF. The collection contains test datasets for two organized sub-tasks: short-term persistence (sub-task A) and long-term persistence (sub-task B). The data for the short-term persistence sub-task was collected over July 2022 and this dataset contains 1,593,376 documents and 882 queries. The data for the long-term persistence sub-task was collected over September 2022 and this dataset consists of 1,081,334 documents and 923 queries. Apart from the original French versions of the webpages and queries, the collection also contains their translations into English.
Rights:: Qwant LongEval Attribution-NonCommercial-ShareAlike License, https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License, and PUB

20. LongEval Train Collection

Creator:: Galuščáková, Petra, Devaud, Romain, Gonzalez-Saez, Gabriela, Mulhem, Philippe, Goeuriot, Lorraine, Piroi, Florina, and Popel, Martin
Publisher:: Université Grenoble Alpes, Qwant, Research Studios Austria, and Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: information retrieval, parallel corpus, search, and automatic evaluation
Language:: French and English
Description:: The collection consists of queries and documents provided by the Qwant search Engine (https://www.qwant.com). The queries, which were issued by the users of Qwant, are based on the selected trending topics. The documents in the collection were selected with respect to these queries using the Qwant click model. Apart from the documents selected using this model, the collection also contains randomly selected documents from the Qwant index. All the data were collected over June 2022. In total, the collection contains 672 train queries, with corresponding 9656 assessments coming from the Qwant click model, and 98 heldout queries. The set of documents consist of 1,570,734 downloaded, cleaned and filtered Web Pages. Apart from their original French versions, the collection also contains translations of the webpages and queries into English. The collection serves as the official training collection for the 2023 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF.
Rights:: Qwant LongEval Attribution-NonCommercial-ShareAlike License, PUB, and https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License

21. Manual Re-evaluation of Translation Quality of WMT 2018 English-Czech systems

Creator:: Popel, Martin, Tomková, Markéta, and Tomek, Jakub
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: machine translation, manual evaluation, fluency, adequacy, and Translation Turing test
Language:: Czech and English
Description:: This data set contains four types of manual annotation of translation quality, focusing on the comparison of human and machine translation quality (aka human-parity). The machine translation system used is English-Czech CUNI Transformer (CUBBITT). The annotations distinguish adequacy, fluency and overall quality. One of the types is Translation Turing test - detecting whether the annotators can distinguish human from machine translation. All the sentences are taken from the English-Czech test set newstest2018 (WMT2018 News translation shared task www.statmt.org/wmt18/translation-task.html), but only from the half with originally English sentences translated to Czech by a professional agency.
Rights:: Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB

22. Optimal reference translation of English-Czech WMT2020

Creator:: Kloudová, Věra, Mraček, David, Bojar, Ondřej, and Popel, Martin
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: translational equivalence, reference translation, optimal reference translation, and WMT
Language:: Czech and English
Description:: We define "optimal reference translation" as a translation thought to be the best possible that can be achieved by a team of human translators. Optimal reference translations can be used in assessments of excellent machine translations. We selected 50 documents (online news articles, with 579 paragraphs in total) from the 130 English documents included in the WMT2020 news test (http://www.statmt.org/wmt20/) with the aim to preserve diversity (style, genre etc.) of the selection. In addition to the official Czech reference translation provided by the WMT organizers (P1), we hired two additional translators (P2 and P3, native Czech speakers) via a professional translation agency, resulting in three independent translations. The main contribution of this dataset are two additional translations (i.e. optimal reference translations N1 and N2), done jointly by two translators-cum-theoreticians with an extreme care for various aspects of translation quality, while taking into account the translations P1-P3. We publish also internal comments (in Czech) for some of the segments. Translation N1 should be closer to the English original (with regards to the meaning and linguistic structure) and female surnames use the Czech feminine suffix (e.g. "Mai" is translated as "Maiová"). Translation N2 is more free, trying to be more creative, idiomatic and entertaining for the readers and following the typical style used in Czech media, while still preserving the rules of functional equivalence. Translation N2 is missing for the segments where it was not deemed necessary to provide two alternative translations. For applications/analyses needing translation of all segments, this should be interpreted as if N2 is the same as N1 for a given segment. We provide the dataset in two formats: OpenDocument spreadsheet (odt) and plain text (one file for each translation and the English original). Some words were highlighted using different colors during the creation of optimal reference translations; this highlighting and comments are present only in the odt format (some comments refer to row numbers in the odt file). Documents are separated by empty lines and each document starts with a special line containing the document name (e.g. "# upi.205735"), which allows alignment with the original WMT2020 news test. For the segments where N2 translations are missing in the odt format, the respective N1 segments are used instead in the plain-text format.
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

23. Optimal Reference Translations from English to Czech

Creator:: Zouhar, Vilém, Kloudová, Věra, Popel, Martin, and Bojar, Ondřej
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: translation, evaluation, and optimal reference translation
Language:: English and Czech
Description:: This corpus contains annotations of translation quality from English to Czech in seven categories on both segment- and document-level. There are 20 documents in total, each with 4 translations (evaluated by each annotator in paralel) of 8 segments (can be longer than one sentence). Apart from the evaluation, the annotators also proposed their own, improved versions of the translations. There were 11 annotators in total, on expertise levels ranging from non-experts to professional translators.
Rights:: Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB

24. Prague Dependency Treebank - Consolidated 1.0 (PDT-C 1.0)

Creator:: Hajič, Jan, Bejček, Eduard, Bémová, Alevtina, Buráňová, Eva, Fučíková, Eva, Hajičová, Eva, Havelka, Jiří, Hlaváčová, Jaroslava, Homola, Petr, Ircing, Pavel, Kárník, Jiří, Kettnerová, Václava, Klyueva, Natalia, Kolářová, Veronika, Kučová, Lucie, Lopatková, Markéta, Mareček, David, Mikulová, Marie, Mírovský, Jiří, Nedoluzhko, Anna, Novák, Michal, Pajas, Petr, Panevová, Jarmila, Peterek, Nino, Poláková, Lucie, Popel, Martin, Popelka, Jan, Romportl, Jan, Rysová, Magdaléna, Semecký, Jiří, Sgall, Petr, Spoustová, Johanka, Straka, Milan, Straňák, Pavel, Synková, Pavlína, Ševčíková, Magda, Šindlerová, Jana, Štěpánek, Jan, Štěpánková, Barbora, Toman, Josef, Urešová, Zdeňka, Vidová Hladká, Barbora, Zeman, Daniel, Zikánová, Šárka, and Žabokrtský, Zdeněk
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: treebank, dependency, tectogrammatics, topic-focus articulation, multiword expressions, coreference, bridging relations, discourse, morphology, syntax, tokenization, lemmatization, semantic relations, lexical semantics, lexicon, valency, speech reconstruction, clauses, speech recognition, and spoken corpus
Language:: Czech
Description:: A richly annotated and genre-diversified language resource, The Prague Dependency Treebank – Consolidated 1.0 (PDT-C 1.0, or PDT-C in short in the sequel) is a consolidated release of the existing PDT-corpora of Czech data, uniformly annotated using the standard PDT scheme. PDT-corpora included in PDT-C: Prague Dependency Treebank (the original PDT contents, written newspaper and journal texts from three genres); Czech part of Prague Czech-English Dependency Treebank (translated financial texts, from English), Prague Dependency Treebank of Spoken Czech (spoken data, including audio and transcripts and multiple speech reconstruction annotation); PDT-Faust (user-generated texts). The difference from the separately published original treebanks can be briefly described as follows: it is published in one package, to allow easier data handling for all the datasets; the data is enhanced with a manual linguistic annotation at the morphological layer and new version of morphological dictionary is enclosed; a common valency lexicon for all four original parts is enclosed. Documentation provides two browsing and editing desktop tools (TrEd and MEd) and the corpus is also available online for searching using PML-TQ.
Rights:: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

25. QTLeap WSD/NED corpus

Creator:: Agirre, Eneko, Branco, António, Popel, Martin, and Simov, Kiril
Publisher:: University of the Basque Country, UPV/EHU, Faculty of Science, Univeristy of Lisbon, FCUL, Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), and Bulgarian Academy of Sciences, IICT-BAS
Type:: text and corpus
Subject:: annotated corpus and multilingual
Language:: Basque, Bulgarian, Czech, English, Portuguese, and Spanish
Description:: This corpora is part of Deliverable 5.5 of the European Commission project QTLeap FP7-ICT-2013.4.1-610516 (http://qtleap.eu). The texts are Q&A interactions from the real-user scenario (batches 1 and 2). The interactions in this corpus are available in Basque, Bulgarian, Czech, English, Portuguese and Spanish. The texts have been automatically annotated with NLP tools, including Word Sense Disambiguation, Named Entity Disambiguation and Coreference resolution. Please check deliverable D5.6 in http://qtleap.eu/deliverables for more information.
Rights:: Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0), http://creativecommons.org/licenses/by-nc-sa/3.0/, and PUB

26. Synthetic part of CzEng 2.0

Creator:: Popel, Martin
Publisher:: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:: text and corpus
Subject:: parallel corpus
Language:: Czech and English
Description:: CzEng is a sentence-parallel Czech-English corpus compiled at the Institute of Formal and Applied Linguistics (ÚFAL). While the full CzEng 2.0 is freely available for non-commercial research purposes from the project website (https://ufal.mff.cuni.cz/czeng), this release contains only the original monolingual parts of news text (csmono 53M and enmono 79M sentences) with automatic (synthetic) translations by CUBBITT. See the attached README for additional details such as the file format.
Rights:: Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0), http://creativecommons.org/licenses/by-sa/4.0/, and PUB