Difference between revisions of "Finding Missing Cross-Language Links in Wikipedia"

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'''Finding Missing Cross-Language Links in Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Carlos Eduardo M. Moreira and Viviane Pereira Moreira.
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'''Finding Missing Cross-Language Links in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Carlos Eduardo M. Moreira]] and [[Viviane Pereira Moreira]].
  
 
== Overview ==
 
== Overview ==
Wikipedia is a public encyclopedia composed of millions of articles written daily by volunteer authors from different regions of the world. The articles contain links called cross-language links which relate corresponding articles across different languages. This feature is extremely useful for applications that work with automatic translation and multilingual information retrieval as it allows the assembly of comparable corpora. Thus, it is important to have a mechanism that automatically creates such links. This has been motivating the development of techniques to identify missing cross-language links. In this article, authors present CLLFinder, an approach for finding missing cross-language links. The approach makes use of the links between categories and of the transitivity between existing cross-language links, as well as textual features extracted from the articles. Experiments using one million articles from the English and Portuguese Wikipedias attest the viability of CLLFinder. The results show that approach has a recall of 96% and a precision of 98%, outperforming the baseline system, even though authors employ simpler and fewer features.
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Wikipedia is a public encyclopedia composed of millions of articles written daily by volunteer authors from different regions of the world. The articles contain links called cross-language links which relate corresponding articles across [[different language]]s. This feature is extremely useful for applications that work with automatic translation and [[multilingual]] [[information retrieval]] as it allows the assembly of comparable corpora. Thus, it is important to have a mechanism that automatically creates such links. This has been motivating the development of techniques to identify missing cross-language links. In this article, authors present CLLFinder, an approach for finding missing cross-language links. The approach makes use of the links between [[categories]] and of the transitivity between existing cross-language links, as well as textual [[features]] extracted from the articles. Experiments using one million articles from the English and Portuguese [[Wikipedia]]s attest the viability of CLLFinder. The results show that approach has a recall of 96% and a precision of 98%, outperforming the baseline system, even though authors employ simpler and fewer features.

Revision as of 08:40, 6 June 2019

Finding Missing Cross-Language Links in Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Carlos Eduardo M. Moreira and Viviane Pereira Moreira.

Overview

Wikipedia is a public encyclopedia composed of millions of articles written daily by volunteer authors from different regions of the world. The articles contain links called cross-language links which relate corresponding articles across different languages. This feature is extremely useful for applications that work with automatic translation and multilingual information retrieval as it allows the assembly of comparable corpora. Thus, it is important to have a mechanism that automatically creates such links. This has been motivating the development of techniques to identify missing cross-language links. In this article, authors present CLLFinder, an approach for finding missing cross-language links. The approach makes use of the links between categories and of the transitivity between existing cross-language links, as well as textual features extracted from the articles. Experiments using one million articles from the English and Portuguese Wikipedias attest the viability of CLLFinder. The results show that approach has a recall of 96% and a precision of 98%, outperforming the baseline system, even though authors employ simpler and fewer features.