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Start Over Contributor Ministerstvo školství, mládeže a tělovýchovy České republiky@@LM2015071@@LINDAT/CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat@@nationalFunds@@ Language Slovak Publisher Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL) Rights Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Type toolService

1. Slavic Forest, Norwegian Wood (models)

Creator:
Rosa, Rudolf, Zeman, Daniel, Mareček, David, and Žabokrtský, Zdeněk
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
other and toolService
Subject:
parsing, dependency parser, cross-lingual parsing, and universal dependencies
Language:
Slovak, Croatian, and Norwegian
Description:
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
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

2. Universal Dependencies 2.4 Models for UDPipe (2019-05-31)

Creator:
Straka, Milan and Straková, Jana
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
tokenizer, POS tagger, lemmatization, tagger, parser, and dependency parser
Language:
Czech, Afrikaans, Arabic, Belarusian, Bulgarian, Catalan, Church Slavic, Coptic, Danish, German, Modern Greek (1453-), English, Estonian, Basque, Persian, Finnish, French, Old French (842-ca. 1400), Irish, Galician, Gothic, Ancient Greek (to 1453), Hebrew, Hindi, Croatian, Hungarian, Armenian, Indonesian, Italian, Japanese, Kazakh, Korean, Latin, Latvian, Lithuanian, Literary Chinese, Marathi, Maltese, Dutch, Norwegian Nynorsk, Norwegian Bokmål, Old Russian, Polish, Portuguese, Romanian, Russian, Sanskrit, Slovak, Slovenian, Northern Sami, Spanish, Serbian, Swedish, Tamil, Telugu, Turkish, Uighur, Ukrainian, Urdu, Vietnamese, Gambian Wolof, Wolof, and Chinese
Description:
Tokenizer, POS Tagger, Lemmatizer and Parser models for 90 treebanks of 60 languages of Universal Depenencies 2.4 Treebanks, created solely using UD 2.4 data (http://hdl.handle.net/11234/1-2988). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_24_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.
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

3. Universal Dependencies 2.5 Models for UDPipe (2019-12-06)

Creator:
Straka, Milan and Straková, Jana
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
tokenizer, POS tagger, lemmatization, tagger, parser, and dependency parser
Language:
Czech, Afrikaans, Arabic, Belarusian, Bulgarian, Catalan, Church Slavic, Coptic, Danish, German, Modern Greek (1453-), English, Estonian, Basque, Persian, Finnish, French, Old French (842-ca. 1400), Irish, Galician, Gothic, Ancient Greek (to 1453), Hebrew, Hindi, Croatian, Hungarian, Armenian, Indonesian, Italian, Japanese, Kazakh, Korean, Latin, Latvian, Lithuanian, Literary Chinese, Marathi, Maltese, Dutch, Norwegian Nynorsk, Norwegian Bokmål, Old Russian, Polish, Portuguese, Romanian, Russian, Sanskrit, Slovak, Slovenian, Northern Sami, Spanish, Serbian, Swedish, Tamil, Telugu, Turkish, Uighur, Ukrainian, Urdu, Vietnamese, Gambian Wolof, Wolof, Chinese, and Scottish Gaelic
Description:
Tokenizer, POS Tagger, Lemmatizer and Parser models for 94 treebanks of 61 languages of Universal Depenencies 2.5 Treebanks, created solely using UD 2.5 data (http://hdl.handle.net/11234/1-3105). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_25_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.
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

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