<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="qasemizadeh-etal-2017-projection">
    <titleInfo>
        <title>Projection Aléatoire Non-Négative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Behrang</namePart>
        <namePart type="family">Qasemizadeh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Laura</namePart>
        <namePart type="family">Kallmeyer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aurelie</namePart>
        <namePart type="family">Herbelot</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iris</namePart>
            <namePart type="family">Eshkol-Taravella</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jean-Yves</namePart>
            <namePart type="family">Antoine</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ATALA</publisher>
            <place>
                <placeTerm type="text">Orléans, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique. We show that weighting methods such as positive pointwise mutual information (PPMI) can be applied to our models after their construction and at a reduced dimensionality. Hence, the proposed technique can efficiently transfer words onto semantically discriminative spaces while demonstrating high computational performance, besides benefits such as ease of update and a simple mechanism for interoperability. We report the performance of our method on several tasks and show that it yields competitive results compared to neural embedding methods in monolingual corpus-based setups.</abstract>
    <identifier type="citekey">qasemizadeh-etal-2017-projection</identifier>
    <location>
        <url>https://aclanthology.org/2017.jeptalnrecital-long.8/</url>
    </location>
    <part>
        <date>2017-6</date>
        <extent unit="page">
            <start>109</start>
            <end>122</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Projection Aléatoire Non-Négative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding
%A Qasemizadeh, Behrang
%A Kallmeyer, Laura
%A Herbelot, Aurelie
%Y Eshkol-Taravella, Iris
%Y Antoine, Jean-Yves
%S Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs
%D 2017
%8 June
%I ATALA
%C Orléans, France
%F qasemizadeh-etal-2017-projection
%X Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique. We show that weighting methods such as positive pointwise mutual information (PPMI) can be applied to our models after their construction and at a reduced dimensionality. Hence, the proposed technique can efficiently transfer words onto semantically discriminative spaces while demonstrating high computational performance, besides benefits such as ease of update and a simple mechanism for interoperability. We report the performance of our method on several tasks and show that it yields competitive results compared to neural embedding methods in monolingual corpus-based setups.
%U https://aclanthology.org/2017.jeptalnrecital-long.8/
%P 109-122

Markdown (Informal)

[Projection Aléatoire Non-Négative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding](https://aclanthology.org/2017.jeptalnrecital-long.8/) (Qasemizadeh et al., JEP/TALN/RECITAL 2017)

• Projection Aléatoire Non-Négative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding (Qasemizadeh et al., JEP/TALN/RECITAL 2017)

ACL

• Behrang Qasemizadeh, Laura Kallmeyer, and Aurelie Herbelot. 2017. Projection Aléatoire Non-Négative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding. In Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs, pages 109–122, Orléans, France. ATALA.