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1. MSTperl delexicalized parser transfer scripts and configuration files

Creator:
Rosa, Rudolf
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
suiteOfTools and toolService
Subject:
parsing and delexicalized parser transfer
Description:
This is a set of MSTperl parser configuration files and scripts for delexicalized parser transfer. They were used in the work reported in arXiv:1506.04897 (http://arxiv.org/abs/1506.04897), as well as several related papers. The MSTperl parser is available at http://hdl.handle.net/11234/1-1480
Rights:
GNU General Public Licence, version 3, http://opensource.org/licenses/GPL-3.0, and PUB

2. Slavic Forest, Norwegian Wood (scripts)

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:
suiteOfTools and toolService
Subject:
parsing, dependency parser, universal dependencies, and cross-lingual parsing
Language:
Czech, Slovak, Slovenian, Croatian, Danish, Swedish, and Norwegian
Description:
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.
Rights:
GNU General Public License 2 or later (GPL-2.0), http://opensource.org/licenses/GPL-2.0, and PUB