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        <namePart type="given">Behrang</namePart>
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        <namePart type="given">Laura</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Iris</namePart>
            <namePart type="family">Eshkol-Taravella</namePart>
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        <name type="personal">
            <namePart type="given">Jean-Yves</namePart>
            <namePart type="family">Antoine</namePart>
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    <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>
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%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.