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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 IWPT 2021 Shared Task Data and System Outputs Zeman, Daniel; Bouma, Gosse; Seddah, Djamé. - : Universal Dependencies Consortium, 2021 BASE Show details 2 IWPT 2020 Shared Task Data and System Outputs Zeman, Daniel; Bouma, Gosse; Seddah, Djamé. - : Universal Dependencies Consortium, 2020 BASE Show details 3 CoNLL 2017 and 2018 Shared Task Blind and Preprocessed Test Data Zeman, Daniel; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 4 Open SDP 1.2 Flickinger, Dan; Hajič, Jan; Ivanova, Angelina. - : Oslo University, 2017. : Charles University, 2017 BASE Show details 5 Slavic Forest, Norwegian Wood (scripts) Rosa, Rudolf; Zeman, Daniel; Mareček, David. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 6 Slavic Forest, Norwegian Wood (models) Rosa, Rudolf; Zeman, Daniel; Mareček, David; Žabokrtský, Zdeněk. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 Abstract: 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 Keyword: cross-lingual parsing; dependency parser; parsing; universal dependencies URL: http://hdl.handle.net/11234/1-1971 BASE Hide details 7 CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies Çöltekin, Çağrı; Kayadelen, Tolga; Droganova, Kira. - : Association for Computational Linguistics, 2017. : country:USA, 2017. : place:Stroudsburg, PA, 2017 BASE Show details 8 Open SDP Flickinger, Dan; Hajič, Jan; Ivanova, Angelina. - : Oslo University, 2016. : Charles University, 2016 BASE Show details

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