Exploiting Wikipedia and EuroWordNet to solve Cross-Lingual Question Answering

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Exploiting Wikipedia and EuroWordNet to solve Cross-Lingual Question Answering is a 2009 publication written in English by Sergio Ferrández, Antonio Toral, Óscar Ferrández, Antonio Ferrández, Rafael Muñoz and published in Information Sciences.

[edit] Abstract

This paper describes a new advance in solving Cross-Lingual Question Answering (CL–QA) tasks. It is built on three main pillars: (i) the use of several multilingual knowledge resources to reference words between languages (the Inter Lingual Index (ILI) module of EuroWordNet and the multilingual knowledge encoded in Wikipedia); (ii) the consideration of more than only one translation per word in order to search candidate answers; and (iii) the analysis of the question in the original language without any translation process. This novel approach overcomes the errors caused by the common use of Machine Translation (MT) services by CL–QA systems. We also expose some studies and experiments that justify the importance of analyzing whether a Named Entity should be translated or not. Experimental results in bilingual scenarios show that our approach performs better than an MT based CL–QA approach achieving an average improvement of 36.7%.

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Exploiting Wikipedia and EuroWordNet to solve Cross-Lingual Question Answering is a 2009 journal article by Sergio Ferrandez, Antonio Toral, Oscar Ferrandez, Antonio Ferrandez, Rafael Munoz and published in Information Sciences.

[edit] Abstract

This paper describes a new advance in solving {Cross-Lingual} Question Answering {(CL-QA)} tasks. It is built on three main pillars: (i) the use of several multilingual knowledge resources to reference words between languages (the Inter Lingual Index {(ILI)} module of {EuroWordNet} and the multilingual knowledge encoded in Wikipedia); (ii) the consideration of more than only one translation per word in order to search candidate answers; and (iii) the analysis of the question in the original language without any translation process. This novel approach overcomes the errors caused by the common use of Machine Translation {(MT)} services by {CL-QA} systems. We also expose some studies and experiments that justify the importance of analyzing whether a Named Entity should be translated or not. Experimental results in bilingual scenarios show that our approach performs better than an {MT} based {CL-QA} approach achieving an average improvement of 36.7\%. 2009 Elsevier Inc. All rights reserved.

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