Difference between revisions of "Exploiting Wikipedia and Eurowordnet to Solve Cross-Lingual Question Answering"
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| + | | title = Exploiting Wikipedia and Eurowordnet to Solve Cross-Lingual Question Answering | ||
| + | | date = 2009 | ||
| + | | authors = [[Sergio Ferrández]]<br />[[Antonio Toral]]<br />[[íscar Ferrández]]<br />[[Antonio Ferrández]]<br />[[Rafael Muñoz]] | ||
| + | | doi = 10.1016/j.ins.2009.06.031 | ||
| + | | link = http://www.sciencedirect.com/science/article/pii/S0020025509003004 | ||
| + | }} | ||
'''Exploiting Wikipedia and Eurowordnet to Solve Cross-Lingual Question Answering''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sergio Ferrández]], [[Antonio Toral]], [[íscar Ferrández]], [[Antonio Ferrández]] and [[Rafael Muñoz]]. | '''Exploiting Wikipedia and Eurowordnet to Solve Cross-Lingual Question Answering''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sergio Ferrández]], [[Antonio Toral]], [[íscar Ferrández]], [[Antonio Ferrández]] and [[Rafael Muñoz]]. | ||
== Overview == | == Overview == | ||
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 Euro[[WordNet]] 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. Authors 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 approach performs better than an MT based CL-QA approach achieving an average improvement of 36.7%. | 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 Euro[[WordNet]] 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. Authors 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 approach performs better than an MT based CL-QA approach achieving an average improvement of 36.7%. | ||
Revision as of 09:01, 15 May 2020
Authors | Sergio Ferrández Antonio Toral íscar Ferrández Antonio Ferrández Rafael Muñoz |
|---|---|
Publication date | 2009 |
DOI | 10.1016/j.ins.2009.06.031 |
Links | Original |
Exploiting Wikipedia and Eurowordnet to Solve Cross-Lingual Question Answering - scientific work related to Wikipedia quality published in 2009, written by Sergio Ferrández, Antonio Toral, íscar Ferrández, Antonio Ferrández and Rafael Muñoz.
Overview
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. Authors 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 approach performs better than an MT based CL-QA approach achieving an average improvement of 36.7%.
