Adapting VerbNet to French using existing resources

Quentin Pradet; Laurence Danlos; Gaël de Chalendar

Adapting VerbNet to French using existing resources

Quentin Pradet, Laurence Danlos, Gaël de Chalendar

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesnt exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for diï¬erent purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Français) and a syntactic lexicon (Lexique-Grammaire).

Anthology ID:: L14-1204
Volume:: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:: May
Year:: 2014
Address:: Reykjavik, Iceland
Editors:: Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:: LREC
SIG:
Publisher:: European Language Resources Association (ELRA)
Note:
Pages:: 1122–1126
Language:
URL:: http://www.lrec-conf.org/proceedings/lrec2014/pdf/203_Paper.pdf
DOI:
Bibkey:
Cite (ACL):: Quentin Pradet, Laurence Danlos, and Gaël de Chalendar. 2014. Adapting VerbNet to French using existing resources. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1122–1126, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):: Adapting VerbNet to French using existing resources (Pradet et al., LREC 2014)
Copy Citation:
PDF:: http://www.lrec-conf.org/proceedings/lrec2014/pdf/203_Paper.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{pradet-etal-2014-adapting,
    title = "Adapting {V}erb{N}et to {F}rench using existing resources",
    author = {Pradet, Quentin  and
      Danlos, Laurence  and
      de Chalendar, Ga{\"e}l},
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L14-1204/",
    pages = "1122--1126",
    abstract = {VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesnt exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for di{\"i}{\textlnot}erent purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Fran{\c{c}}ais) and a syntactic lexicon (Lexique-Grammaire).}
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pradet-etal-2014-adapting">
    <titleInfo>
        <title>Adapting VerbNet to French using existing resources</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Quentin</namePart>
        <namePart type="family">Pradet</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Laurence</namePart>
        <namePart type="family">Danlos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gaël</namePart>
        <namePart type="family">de Chalendar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nicoletta</namePart>
            <namePart type="family">Calzolari</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Choukri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thierry</namePart>
            <namePart type="family">Declerck</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hrafn</namePart>
            <namePart type="family">Loftsson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Bente</namePart>
            <namePart type="family">Maegaard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joseph</namePart>
            <namePart type="family">Mariani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Asuncion</namePart>
            <namePart type="family">Moreno</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Odijk</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>European Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Reykjavik, Iceland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesnt exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for diï¬erent purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Français) and a syntactic lexicon (Lexique-Grammaire).</abstract>
    <identifier type="citekey">pradet-etal-2014-adapting</identifier>
    <location>
        <url>https://aclanthology.org/L14-1204/</url>
    </location>
    <part>
        <date>2014-05</date>
        <extent unit="page">
            <start>1122</start>
            <end>1126</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Adapting VerbNet to French using existing resources
%A Pradet, Quentin
%A Danlos, Laurence
%A de Chalendar, Gaël
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F pradet-etal-2014-adapting
%X VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesnt exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for diï¬erent purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Français) and a syntactic lexicon (Lexique-Grammaire).
%U https://aclanthology.org/L14-1204/
%P 1122-1126

Download as File

Markdown (Informal)

[Adapting VerbNet to French using existing resources](https://aclanthology.org/L14-1204/) (Pradet et al., LREC 2014)

Adapting VerbNet to French using existing resources (Pradet et al., LREC 2014)

ACL

Quentin Pradet, Laurence Danlos, and Gaël de Chalendar. 2014. Adapting VerbNet to French using existing resources. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1122–1126, Reykjavik, Iceland. European Language Resources Association (ELRA).