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|Title:||Document similarity for Arabic and cross-lingual Web content||Authors:||Salhi, Ali
|Keywords:||Cross-language information retrieval;Explicit Semantic Association;Similarity (Language learning);Information retrieval - Arab countries||Issue Date:||11-Nov-2017||Publisher:||Springer Verlag||Series/Report no.:||Communications in Computer and Information Science #782||Abstract:||Document similarity is basic for Information Retrieval. Cross Lin- gual (CL) similarity is important for many data processing tasks such as CL palgiarism detection and retrieval and document quality assessment. We study CL similarity based on the Explicit Semantic Association (ESA) adapted to a cross lingual setting with focus on Arabic. We compare the degree to which CL similarity testing performs where one of the language is Arabic with its monolingual counterpart for various text chunk sizes. We describe the used infrastructure and report on some of the testing results, study the possible sources of encountered weaknesses and point to the possible directions for improvement.||URI:||http://hdl.handle.net/20.500.11889/5350||ISSN:||1865-0937|
|Appears in Collections:||Fulltext Publications|
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