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}
}
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