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Advances in Natural Language Processing

Volume 8686 of the series Lecture Notes in Computer Science pp 105-115

Cross-Lingual Semantic Similarity Measure for Comparable Articles

  • Motaz SaadAffiliated withSMarT Group, LORIA INRIAUniversité de Lorraine, LORIA, UMR 7503CNRS, LORIA, UMR 7503
  • , David LangloisAffiliated withSMarT Group, LORIA INRIAUniversité de Lorraine, LORIA, UMR 7503CNRS, LORIA, UMR 7503
  • , Kamel SmaïliAffiliated withSMarT Group, LORIA INRIAUniversité de Lorraine, LORIA, UMR 7503CNRS, LORIA, UMR 7503

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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.

Keywords

Cross-lingual latent semantic indexing corpus comparability cross-lingual information retrieval