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)
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.
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.
Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/).
For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive.
Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions.
Update 2018-09-03
===============
Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.
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).
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-2988). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3105). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3226). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3424). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3687). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Changes in version 1.1:
1. Universal Dependencies tagset instead of the older and smaller Google Universal POS tagset.
2. SVM classifier trained on Universal Dependencies 1.2 instead of HamleDT 2.0.
3. Balto-Slavic languages, Germanic languages and Romance languages were tagged by classifier trained only on the respective group of languages. Other languages were tagged by a classifier trained on all available languages. The "c7" combination from version 1.0 is no longer used.
DZ Interset is a means of converting among various tag sets in natural language processing. The core idea is similar to interlingua-based machine translation. DZ Interset defines a set of features that are encoded by the various tag sets. The set of features should be as universal as possible. It does not need to encode everything that is encoded by any tag set but it should encode all information that people may want to access and/or port from one tag set to another.
New tag sets are attached by writing a driver for them. Once the driver is ready, you can easily convert tags between the new set and any other set for which you also have a driver. This reusability is an obvious advantage over writing a targeted conversion procedure each time you need to convert between a particular pair of tag sets. and grant MSM 0021620838 of the Ministry of Education of the Czech Republic
English-Hindi parallel corpus collected from several sources. Tokenized and sentence-aligned. A part of the data is our patch for the Emille parallel corpus. and FP7-ICT-2007-3-231720 (EuroMatrix Plus) 7E09003 (Czech part of EM+)
English-Urdu parallel corpus is a collection of religious texts (Quran, Bible) in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation. Our modifications of crawled data include but are not limited to the following:
1- Manually corrected sentence alignment of the corpora.
2- Our data split (training-development-test) so that our published experiments can be reproduced.
3- Tokenization (optional, but needed to reproduce our experiments).
4- Normalization (optional) of e.g. European vs. Urdu numerals, European vs. Urdu punctuation, removal of Urdu diacritics.
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.
HindEnCorp parallel texts (sentence-aligned) come from the following sources:
Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008).
Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi.
EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages.
Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus.

For the current release, we are extending the parallel corpus using these sources:
Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi.
TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available.
The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus.
Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files.
Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary. and LM2010013,
A Hindi corpus of texts downloaded mostly from news sites. Contains both the original raw texts and an extensively cleaned-up and tokenized version suitable for language modeling. 18M sentences, 308M tokens and FP7-ICT-2007-3-231720 (EuroMatrix Plus), 7E09003 (Czech part of EM+)
Hindi monolingual corpus. It is based primarily on web crawls performed using various tools and at various times. Since the web is a living data source, we treat these crawls as completely separate sources, despite they may overlap. To estimate the magnitude of this overlap, we compared the total number of segments if we concatenate the individual sources (each source being deduplicated on its own) with the number of segments if we de-duplicate all sources to- gether. The difference is just around 1%, confirming, that various web crawls (or their subsequent processings) differ significantly.
HindMonoCorp contains data from:
Hindi web texts, a monolingual corpus containing mainly Hindi news articles has already been collected and released by Bojar et al. (2008). We use the HTML files as crawled for this corpus in 2010 and we add a small crawl performed in 2013 and re-process them with the current pipeline. These sources are denoted HWT 2010 and HWT 2013 in the following.
Hindi corpora in W2C have been collected by Martin Majliš during his project to automatically collect corpora in many languages (Majliš and Žabokrtský, 2012). There are in fact two corpora of Hindi available—one from web harvest (W2C Web) and one from the Wikipedia (W2C Wiki).
SpiderLing is a web crawl carried out during November and December 2013 using SpiderLing (Suchomel and Pomikálek, 2012). The pipeline includes extraction of plain texts and deduplication at the level of documents, see below.
CommonCrawl is a non-profit organization that regu- larly crawls the web and provides anyone with the data. We are grateful to Christian Buck for extracting plain text Hindi segments from the 2012 and 2013-fall crawls for us.
Intercorp – 7 books with their translations scanned and manually alligned per paragraph
RSS Feeds from Webdunia.com and the Hindi version of BBC International followed by our custom crawler from September 2013 till January 2014. and LM2010013,
This package contains data used in the IWPT 2020 shared task. It contains training, development and test (evaluation) datasets. The data is based on a subset of Universal Dependencies release 2.5 (http://hdl.handle.net/11234/1-3105) but some treebanks contain additional enhanced annotations. Moreover, not all of these additions became part of Universal Dependencies release 2.6 (http://hdl.handle.net/11234/1-3226), which makes the shared task data unique and worth a separate release to enable later comparison with new parsing algorithms. The package also contains a number of Perl and Python scripts that have been used to process the data during preparation and during the shared task. Finally, the package includes the official primary submission of each team participating in the shared task.
This package contains data used in the IWPT 2021 shared task. It contains training, development and test (evaluation) datasets. The data is based on a subset of Universal Dependencies release 2.7 (http://hdl.handle.net/11234/1-3424) but some treebanks contain additional enhanced annotations. Moreover, not all of these additions became part of Universal Dependencies release 2.8 (http://hdl.handle.net/11234/1-3687), which makes the shared task data unique and worth a separate release to enable later comparison with new parsing algorithms. The package also contains a number of Perl and Python scripts that have been used to process the data during preparation and during the shared task. Finally, the package includes the official primary submission of each team participating in the shared task.
Lingua::Interset is a universal morphosyntactic feature set to which all tagsets of all corpora/languages can be mapped. Version 2.026 covers 37 different tagsets of 21 languages. Limited support of the older drivers for other languages (which are not included in this package but are available for download elsewhere) is also available; these will be fully ported to Interset 2 in future.
Interset is implemented as Perl libraries. It is also available via CPAN.
This multilingual resource contains corpora for 14 languages, gathered at the occasion of the 1.2 edition of the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). These corpora were meant to serve as additional "raw" corpora, to help discovering unseen verbal MWEs.
The corpora are provided in CONLL-U (https://universaldependencies.org/format.html) format. They contain morphosyntactic annotations (parts of speech, lemmas, morphological features, and syntactic dependencies). Depending on the language, the information comes from treebanks (mostly Universal Dependencies v2.x) or from automatic parsers trained on UD v2.x treebanks (e.g., UDPipe).
VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do).
For the 1.2 shared task edition, the data covers 14 languages, for which VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format.
Morphological and syntactic information – not necessarily using UD tagsets – including 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, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.2 (2020). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.2
This tool is the first morphological analyzer ever for this language.
The analyzer is a FST that produces all possible segmentations and tagging sequences in a word-by-word fashion.
The original SDP 2014 and 2015 data collections were made available under task-specific ‘evaluation’ licenses to registered SemEval participants. In mid-2016, all original data has been bundled with system submissions, supporting software, an additional SDP-style collection of semantic dependency graphs, and additional background material (from which some of the SDP target representations were derived) for release through the Linguistic Data Consortium (with LDC catalogue number LDC2016 T10).
One of the four English target representations (viz. DM) and the entire Czech data (in the PSD target representation) are not derivative of LDC-licensed annotations and, thus, can be made available for direct download (Open SDP; version 1.1; April 2016) under a more permissive licensing scheme, viz. the Creative Common Attribution-NonCommercial-ShareAlike license. This package also includes some ‘richer’ meaning representations from which the English bi-lexical DM graphs derive, viz. scope-underspecified logical forms and more abstract, non-lexicalized ‘semantic networks’. The latter of these are formally (if not linguistically) similar to Abstract Meaning Representation (AMR) and are available in a range of serializations, including in AMR-like syntax.
Please use the following bibliographic reference for the SDP 2016 data:
@string{C:LREC = {{I}nternational {C}onference on
{L}anguage {R}esources and {E}valuation}}
@string{LREC:16 = {Proceedings of the 10th } # C:LREC}
@string{L:LREC:16 = {Portoro\v{z}, Slovenia}}
@inproceedings{Oep:Kuh:Miy:16,
author = {Oepen, Stephan and Kuhlmann, Marco and Miyao, Yusuke
and Zeman, Daniel and Cinkov{\'a}, Silvie
and Flickinger, Dan and Haji\v{c}, Jan
and Ivanova, Angelina and Ure\v{s}ov{\'a}, Zde\v{n}ka},
title = {Towards Comparability of Linguistic Graph Banks for Semantic Parsing},
booktitle = LREC:16
year = 2016,
address = L:LREC:16,
pages = {3991--3995}
}
The original SDP 2014 and 2015 data collections were made available under task-specific ‘evaluation’ licenses to registered SemEval participants. In mid-2016, all original data has been bundled with system submissions, supporting software, an additional SDP-style collection of semantic dependency graphs, and additional background material (from which some of the SDP target representations were derived) for release through the Linguistic Data Consortium (with LDC catalogue number LDC2016 T10).
One of the four English target representations (viz. DM) and the entire Czech data (in the PSD target representation) are not derivative of LDC-licensed annotations and, thus, can be made available for direct download (Open SDP; version 1.2; January 2017) under a more permissive licensing scheme, viz. the Creative Common Attribution-NonCommercial-ShareAlike license. This package also includes some ‘richer’ meaning representations from which the English bi-lexical DM graphs derive, viz. scope-underspecified logical forms and more abstract, non-lexicalized ‘semantic networks’. The latter of these are formally (if not linguistically) similar to Abstract Meaning Representation (AMR) and are available in a range of serializations, including in AMR-like syntax.
Version 1.1 was released April 2016. Version 1.2 adds the 2015 Turku system, which was accidentally left out from version 1.1.
Please use the following bibliographic reference for the SDP 2016 data:
@string{C:LREC = {{I}nternational {C}onference on
{L}anguage {R}esources and {E}valuation}}
@string{LREC:16 = {Proceedings of the 10th } # C:LREC}
@string{L:LREC:16 = {Portoro\v{z}, Slovenia}}
@inproceedings{Oep:Kuh:Miy:16,
author = {Oepen, Stephan and Kuhlmann, Marco and Miyao, Yusuke
and Zeman, Daniel and Cinkov{\'a}, Silvie
and Flickinger, Dan and Haji\v{c}, Jan
and Ivanova, Angelina and Ure\v{s}ov{\'a}, Zde\v{n}ka},
title = {Towards Comparability of Linguistic Graph Banks for Semantic Parsing},
booktitle = LREC:16
year = 2016,
address = L:LREC:16,
pages = {3991--3995}
}
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.
The Prague Dependency Treebank 3.5 is the 2018 edition of the core Prague Dependency Treebank (PDT). It contains all PDT annotation made at the Institute of Formal and Applied Linguistics under various projects between 1996 and 2018 on the original texts, i.e., all annotation from PDT 1.0, PDT 2.0, PDT 2.5, PDT 3.0, PDiT 1.0 and PDiT 2.0, plus corrections, new structure of basic documentation and new list of authors covering all previous editions. The Prague Dependency Treebank 3.5 (PDT 3.5) contains the same texts as the previous versions since 2.0; there are 49,431 annotated sentences (832,823 words) on all layers, from tectogrammatical annotation to syntax to morphology. There are additional annotated sentences for syntax and morphology; the totals for the lower layers of annotation are: 87,913 sentences with 1,502,976 words at the analytical layer (surface dependency syntax) and 115,844 sentences with 1,956,693 words at the morphological layer of annotation (these totals include the annotation with the higher layers annotated as well). Closely linked to the tectogrammatical layer is the annotation of sentence information structure, multiword expressions, coreference, bridging relations and discourse relations.
Trained models for UDPipe used to produce our final submission to the Vardial 2017 CLP shared task (https://bitbucket.org/hy-crossNLP/vardial2017). The SK model was trained on CS data, the HR model on SL data, and the SV model on a concatenation of DA and NO data. The scripts and commands used to create the models are part of separate submission (http://hdl.handle.net/11234/1-1970).
The models were trained with UDPipe version 3e65d69 from 3rd Jan 2017, obtained from
https://github.com/ufal/udpipe -- their functionality with newer or older versions of UDPipe is not guaranteed.
We list here the Bash command sequences that can be used to reproduce our results submitted to VarDial 2017. The input files must be in CoNLLU format. The models only use the form, UPOS, and Universal Features fields (SK only uses the form). You must have UDPipe installed. The feats2FEAT.py script, which prunes the universal features, is bundled with this submission.
SK -- tag and parse with the model:
udpipe --tag --parse sk-translex.v2.norm.feats07.w2v.trainonpred.udpipe sk-ud-predPoS-test.conllu
A slightly better after-deadline model (sk-translex.v2.norm.Case-feats07.w2v.trainonpred.udpipe), which we mention in the accompanying paper, is also included. It is applied in the same way (udpipe --tag --parse sk-translex.v2.norm.Case-feats07.w2v.trainonpred.udpipe sk-ud-predPoS-test.conllu).
HR -- prune the Features to keep only Case and parse with the model:
python3 feats2FEAT.py Case < hr-ud-predPoS-test.conllu | udpipe --parse hr-translex.v2.norm.Case.w2v.trainonpred.udpipe
NO -- put the UPOS annotation aside, tag Features with the model, merge with the left-aside UPOS annotation, and parse with the model (this hassle is because UDPipe cannot be told to keep UPOS and only change Features):
cut -f1-4 no-ud-predPoS-test.conllu > tmp
udpipe --tag no-translex.v2.norm.tgttagupos.srctagfeats.Case.w2v.udpipe no-ud-predPoS-test.conllu | cut -f5- | paste tmp - | sed 's/^\t$//' | udpipe --parse no-translex.v2.norm.tgttagupos.srctagfeats.Case.w2v.udpipe
Tools and scripts used to create the cross-lingual parsing models submitted to VarDial 2017 shared task (https://bitbucket.org/hy-crossNLP/vardial2017), as described in the linked paper. The trained UDPipe models themselves are published in a separate submission (https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1971).
For each source (SS, e.g. sl) and target (TT, e.g. hr) language,
you need to add the following into this directory:
- treebanks (Universal Dependencies v1.4):
SS-ud-train.conllu
TT-ud-predPoS-dev.conllu
- parallel data (OpenSubtitles from Opus):
OpenSubtitles2016.SS-TT.SS
OpenSubtitles2016.SS-TT.TT
!!! If they are originally called ...TT-SS... instead of ...SS-TT...,
you need to symlink them (or move, or copy) !!!
- target tagging model
TT.tagger.udpipe
All of these can be obtained from https://bitbucket.org/hy-crossNLP/vardial2017
You also need to have:
- Bash
- Perl 5
- Python 3
- word2vec (https://code.google.com/archive/p/word2vec/); we used rev 41 from 15th Sep 2014
- udpipe (https://github.com/ufal/udpipe); we used commit 3e65d69 from 3rd Jan 2017
- Treex (https://github.com/ufal/treex); we used commit d27ee8a from 21st Dec 2016
The most basic setup is the sl-hr one (train_sl-hr.sh):
- normalization of deprels
- 1:1 word-alignment of parallel data with Monolingual Greedy Aligner
- simple word-by-word translation of source treebank
- pre-training of target word embeddings
- simplification of morpho feats (use only Case)
- and finally, training and evaluating the parser
Both da+sv-no (train_ds-no.sh) and cs-sk (train_cs-sk.sh) add some cross-tagging, which seems to be useful only in
specific cases (see paper for details).
Moreover, cs-sk also adds more morpho features, selecting those that
seem to be very often shared in parallel data.
The whole pipeline takes tens of hours to run, and uses several GB of RAM, so make sure to use a powerful computer.
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). This is the second release of UD Treebanks, Version 1.1.
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
This release is special in that the treebanks will be used as training/development data in the CoNLL 2017 shared task (http://universaldependencies.org/conll17/). Test data are not released, except for the few treebanks that do not take part in the shared task. 64 treebanks will be in the shared task, and they correspond to the following 45 languages: Ancient Greek, Arabic, Basque, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Gothic, Greek, Hebrew, Hindi, Hungarian, Indonesian, Irish, Italian, Japanese, Kazakh, Korean, Latin, Latvian, Norwegian, Old Church Slavonic, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Turkish, Ukrainian, Urdu, Uyghur and Vietnamese.
This release fixes a bug in http://hdl.handle.net/11234/1-1976. Changed files: ud-tools-v2.0.tgz (conllu_to_text.pl, conllu_to_conllx.pl; added text_without_spaces.pl), ud-treebanks-conll2017.tgz (fi_ftb-ud-train.txt, he-ud-train.txt, it-ud-train.txt, pt_br-ud-train.txt, es-ud-train.txt) and ud-treebanks-v2.0.tgz (fi_ftb-ud-train.txt, he-ud-train.txt, it-ud-train.txt, pt_br-ud-train.txt, es-ud-train.txt, ar_nyuad-ud-dev.txt, ar_nyuad-ud-test.txt, ar_nyuad-ud-train.txt, cop-ud-dev.txt, cop-ud-test.txt, cop-ud-train.txt, sa-ud-dev.txt, sa-ud-test.txt, sa-ud-train.txt).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
This release contains the test data used in the CoNLL 2017 shared task on parsing Universal Dependencies. Due to the shared task the test data was held hidden and not released together with the training and development data of UD 2.0. Therefore this release complements the UD 2.0 release (http://hdl.handle.net/11234/1-1983) to a full release of UD treebanks. In addition, the present release contains 18 new parallel test sets and 4 test sets in surprise languages. The present release also includes the development data already released with UD 2.0. Unlike regular UD releases, this one uses the folder-file structure that was visible to the systems participating in the shared task.
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).