Difference between revisions of "Mapping Arabic Wikipedia into the Named Entities Taxonomy"

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'''Mapping Arabic Wikipedia into the Named Entities Taxonomy''' - scientific work related to Wikipedia quality published in 2012, written by Fahd Alotaibi and Mark G. Lee.
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'''Mapping Arabic Wikipedia into the Named Entities Taxonomy''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Fahd Alotaibi]] and [[Mark G. Lee]].
  
 
== Overview ==
 
== Overview ==
This paper describes a comprehensive set of experiments conducted in o rder to classify Arabic Wikipedia articles into predefined sets of Named Entity classes. Authors tackle using fou r different classifiers, namely: Naive Bayes, Multinomial Naive Bayes, Support Vector Machin es, and Stochastic Gradient Descent. Authors report on several aspects related to classification models in the sense of feature representation, feature set and statistical modelling. The result s reported show that, authors are able to correctly classify the articles with scores of 90% on Precision, Recall and balanced F-measure.
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This paper describes a comprehensive set of experiments conducted in o rder to classify [[Arabic Wikipedia]] articles into predefined sets of Named Entity classes. Authors tackle using fou r different classifiers, namely: Naive Bayes, Multinomial Naive Bayes, Support Vector Machin es, and Stochastic Gradient Descent. Authors report on several aspects related to classification models in the sense of feature representation, feature set and statistical modelling. The result s reported show that, authors are able to correctly classify the articles with scores of 90% on Precision, Recall and balanced F-measure.

Revision as of 21:53, 14 July 2019

Mapping Arabic Wikipedia into the Named Entities Taxonomy - scientific work related to Wikipedia quality published in 2012, written by Fahd Alotaibi and Mark G. Lee.

Overview

This paper describes a comprehensive set of experiments conducted in o rder to classify Arabic Wikipedia articles into predefined sets of Named Entity classes. Authors tackle using fou r different classifiers, namely: Naive Bayes, Multinomial Naive Bayes, Support Vector Machin es, and Stochastic Gradient Descent. Authors report on several aspects related to classification models in the sense of feature representation, feature set and statistical modelling. The result s reported show that, authors are able to correctly classify the articles with scores of 90% on Precision, Recall and balanced F-measure.