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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; Žabokrtský, Zdeněk. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 Abstract: 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. Keyword: cross-lingual parsing; dependency parser; parsing; universal dependencies URL: http://hdl.handle.net/11234/1-1970 BASE Hide details 6 Slavic Forest, Norwegian Wood (models) Rosa, Rudolf; Zeman, Daniel; Mareček, David. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show 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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