@inproceedings{de-la-clergerie-etal-2017-parisnlp,
title = "The {P}aris{NLP} entry at the {C}on{LL} {UD} Shared Task 2017: A Tale of a {\#}{P}arsing{T}ragedy",
author = "de La Clergerie, {\'E}ric and
Sagot, Beno{\^\i}t and
Seddah, Djam{\'e}",
editor = "Haji{\v{c}}, Jan and
Zeman, Dan",
booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-3026",
doi = "10.18653/v1/K17-3026",
pages = "243--252",
abstract = "We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task{'}s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this {\#}ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="de-la-clergerie-etal-2017-parisnlp">
<titleInfo>
<title>The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy</title>
</titleInfo>
<name type="personal">
<namePart type="given">Éric</namePart>
<namePart type="family">de La Clergerie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Benoît</namePart>
<namePart type="family">Sagot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Djamé</namePart>
<namePart type="family">Seddah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jan</namePart>
<namePart type="family">Hajič</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dan</namePart>
<namePart type="family">Zeman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task’s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this #ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.</abstract>
<identifier type="citekey">de-la-clergerie-etal-2017-parisnlp</identifier>
<identifier type="doi">10.18653/v1/K17-3026</identifier>
<location>
<url>https://aclanthology.org/K17-3026</url>
</location>
<part>
<date>2017-08</date>
<extent unit="page">
<start>243</start>
<end>252</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy
%A de La Clergerie, Éric
%A Sagot, Benoît
%A Seddah, Djamé
%Y Hajič, Jan
%Y Zeman, Dan
%S Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F de-la-clergerie-etal-2017-parisnlp
%X We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task’s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this #ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.
%R 10.18653/v1/K17-3026
%U https://aclanthology.org/K17-3026
%U https://doi.org/10.18653/v1/K17-3026
%P 243-252
Markdown (Informal)
[The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy](https://aclanthology.org/K17-3026) (de La Clergerie et al., CoNLL 2017)
ACL