Cross-Lingual Semantic Similarity Measure for Comparable Articles
Identifieur interne : 000933 ( Main/Exploration ); précédent : 000932; suivant : 000934Cross-Lingual Semantic Similarity Measure for Comparable Articles
Auteurs : Motaz Saad [France] ; David Langlois [France] ; Kamel Smaïli [France]Source :
English descriptors
- mix :
Abstract
A measure of similarity is required to find and compare cross-lingual articles concerning a specific topic. This measure can be based on bilingual dictionaries or based on numerical methods such as Latent Semantic Indexing (LSI). In this paper, we use LSI in two ways to retrieve Arabic-English comparable articles. The first way is monolingual: the English article is translated into Arabic and then mapped into the Arabic LSI space; the second way is cross-lingual: Arabic and English documents are mapped into Arabic-English LSI space. Then we compare LSI approaches to the dictionary-based approach on several English-Arabic parallel and comparable corpora. Results indicate that the performance of our cross-lingual LSI approach is competitive to the monolingual approach and even better for some corpora. Moreover, both LSI approaches outperform the dictionary approach.
Url:
DOI: 10.1007/978-3-319-10888-9_11
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 001996
- to stream Hal, to step Curation: 001996
- to stream Hal, to step Checkpoint: 000869
- to stream Main, to step Merge: 000934
- to stream Main, to step Curation: 000933
Le document en format XML
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1007/978-3-319-10888-9_11</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Cross-lingual latent semantic indexing</term><term>corpus comparability</term><term>cross-lingual information retrieval</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A measure of similarity is required to find and compare cross-lingual articles concerning a specific topic. This measure can be based on bilingual dictionaries or based on numerical methods such as Latent Semantic Indexing (LSI). In this paper, we use LSI in two ways to retrieve Arabic-English comparable articles. The first way is monolingual: the English article is translated into Arabic and then mapped into the Arabic LSI space; the second way is cross-lingual: Arabic and English documents are mapped into Arabic-English LSI space. Then we compare LSI approaches to the dictionary-based approach on several English-Arabic parallel and comparable corpora. Results indicate that the performance of our cross-lingual LSI approach is competitive to the monolingual approach and even better for some corpora. Moreover, both LSI approaches outperform the dictionary approach.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Saad, Motaz" sort="Saad, Motaz" uniqKey="Saad M" first="Motaz" last="Saad">Motaz Saad</name></region><name sortKey="Langlois, David" sort="Langlois, David" uniqKey="Langlois D" first="David" last="Langlois">David Langlois</name><name sortKey="Smaili, Kamel" sort="Smaili, Kamel" uniqKey="Smaili K" first="Kamel" last="Smaïli">Kamel Smaïli</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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