<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="salle-villavicencio-2018-incorporating">
    <titleInfo>
        <title>Incorporating Subword Information into Matrix Factorization Word Embeddings</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alexandre</namePart>
        <namePart type="family">Salle</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aline</namePart>
        <namePart type="family">Villavicencio</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Second Workshop on Subword/Character LEvel Models</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Manaal</namePart>
            <namePart type="family">Faruqui</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hinrich</namePart>
            <namePart type="family">Schütze</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabel</namePart>
            <namePart type="family">Trancoso</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulia</namePart>
            <namePart type="family">Tsvetkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yadollah</namePart>
            <namePart type="family">Yaghoobzadeh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">New Orleans</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The positive effect of adding subword information to word embeddings has been demonstrated for predictive models. In this paper we investigate whether similar benefits can also be derived from incorporating subwords into counting models. We evaluate the impact of different types of subwords (n-grams and unsupervised morphemes), with results confirming the importance of subword information in learning representations of rare and out-of-vocabulary words.</abstract>
    <identifier type="citekey">salle-villavicencio-2018-incorporating</identifier>
    <identifier type="doi">10.18653/v1/W18-1209</identifier>
    <location>
        <url>https://aclanthology.org/W18-1209</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>66</start>
            <end>71</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Incorporating Subword Information into Matrix Factorization Word Embeddings
%A Salle, Alexandre
%A Villavicencio, Aline
%Y Faruqui, Manaal
%Y Schütze, Hinrich
%Y Trancoso, Isabel
%Y Tsvetkov, Yulia
%Y Yaghoobzadeh, Yadollah
%S Proceedings of the Second Workshop on Subword/Character LEvel Models
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans
%F salle-villavicencio-2018-incorporating
%X The positive effect of adding subword information to word embeddings has been demonstrated for predictive models. In this paper we investigate whether similar benefits can also be derived from incorporating subwords into counting models. We evaluate the impact of different types of subwords (n-grams and unsupervised morphemes), with results confirming the importance of subword information in learning representations of rare and out-of-vocabulary words.
%R 10.18653/v1/W18-1209
%U https://aclanthology.org/W18-1209
%U https://doi.org/10.18653/v1/W18-1209
%P 66-71

Markdown (Informal)

[Incorporating Subword Information into Matrix Factorization Word Embeddings](https://aclanthology.org/W18-1209) (Salle & Villavicencio, SCLeM 2018)

• Incorporating Subword Information into Matrix Factorization Word Embeddings (Salle & Villavicencio, SCLeM 2018)

ACL

• Alexandre Salle and Aline Villavicencio. 2018. Incorporating Subword Information into Matrix Factorization Word Embeddings. In Proceedings of the Second Workshop on Subword/Character LEvel Models, pages 66–71, New Orleans. Association for Computational Linguistics.