<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="makrai-2016-filtering">
    <titleInfo>
        <title>Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Márton</namePart>
        <namePart type="family">Makrai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)</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">Sara</namePart>
            <namePart type="family">Goggi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marko</namePart>
            <namePart type="family">Grobelnik</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">Helene</namePart>
            <namePart type="family">Mazo</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">Portorož, Slovenia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Word translations arise in dictionary-like organization as well as via machine learning from corpora. The former is exemplified by Wiktionary, a crowd-sourced dictionary with editions in many languages. Ács et al. (2013) obtain word translations from Wiktionary with the pivot-based method, also called triangulation, that infers word translations in a pair of languages based on translations to other, typically better resourced ones called pivots. Triangulation may introduce noise if words in the pivot are polysemous. The reliability of each triangulated translation is basically estimated by the number of pivot languages (Tanaka et al 1994). Mikolov et al (2013) introduce a method for generating or scoring word translations. Translation is formalized as a linear mapping between distributed vector space models (VSM) of the two languages. VSMs are trained on monolingual data, while the mapping is learned in a supervised fashion, using a seed dictionary of some thousand word pairs. The mapping can be used to associate existing translations with a real-valued similarity score. This paper exploits human labor in Wiktionary combined with distributional information in VSMs. We train VSMs on gigaword corpora, and the linear translation mapping on direct (non-triangulated) Wiktionary pairs. This mapping is used to filter triangulated translations based on scores. The motivation is that scores by the mapping may be a smoother measure of merit than considering only the number of pivot for the triangle. We evaluate the scores against dictionaries extracted from parallel corpora (Tiedemann 2012). We show that linear translation really provides a more reliable method for triangle scoring than pivot count. The methods we use are language-independent, and the training data is easy to obtain for many languages. We chose the German-Hungarian pair for evaluation, in which the filtered triangles resulting from our experiments are the greatest freely available list of word translations we are aware of.</abstract>
    <identifier type="citekey">makrai-2016-filtering</identifier>
    <location>
        <url>https://aclanthology.org/L16-1439</url>
    </location>
    <part>
        <date>2016-05</date>
        <extent unit="page">
            <start>2766</start>
            <end>2770</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models
%A Makrai, Márton
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F makrai-2016-filtering
%X Word translations arise in dictionary-like organization as well as via machine learning from corpora. The former is exemplified by Wiktionary, a crowd-sourced dictionary with editions in many languages. Ács et al. (2013) obtain word translations from Wiktionary with the pivot-based method, also called triangulation, that infers word translations in a pair of languages based on translations to other, typically better resourced ones called pivots. Triangulation may introduce noise if words in the pivot are polysemous. The reliability of each triangulated translation is basically estimated by the number of pivot languages (Tanaka et al 1994). Mikolov et al (2013) introduce a method for generating or scoring word translations. Translation is formalized as a linear mapping between distributed vector space models (VSM) of the two languages. VSMs are trained on monolingual data, while the mapping is learned in a supervised fashion, using a seed dictionary of some thousand word pairs. The mapping can be used to associate existing translations with a real-valued similarity score. This paper exploits human labor in Wiktionary combined with distributional information in VSMs. We train VSMs on gigaword corpora, and the linear translation mapping on direct (non-triangulated) Wiktionary pairs. This mapping is used to filter triangulated translations based on scores. The motivation is that scores by the mapping may be a smoother measure of merit than considering only the number of pivot for the triangle. We evaluate the scores against dictionaries extracted from parallel corpora (Tiedemann 2012). We show that linear translation really provides a more reliable method for triangle scoring than pivot count. The methods we use are language-independent, and the training data is easy to obtain for many languages. We chose the German-Hungarian pair for evaluation, in which the filtered triangles resulting from our experiments are the greatest freely available list of word translations we are aware of.
%U https://aclanthology.org/L16-1439
%P 2766-2770

Markdown (Informal)

[Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models](https://aclanthology.org/L16-1439) (Makrai, LREC 2016)

• Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models (Makrai, LREC 2016)

ACL

• Márton Makrai. 2016. Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2766–2770, Portorož, Slovenia. European Language Resources Association (ELRA).