Classifying Wikipedia Articles into Ne's Using Svm's with Threshold Adjustment
Authors | Iman Saleh Kareem Darwish Aly A. Fahmy |
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Publication date | 2010 |
Links | Original |
Classifying Wikipedia Articles into Ne's Using Svm's with Threshold Adjustment - scientific work related to Wikipedia quality published in 2010, written by Iman Saleh, Kareem Darwish and Aly A. Fahmy.
Overview
In this paper, a method is presented to recognize multilingual Wikipedia named entity articles. This method classifies multilingual Wikipedia articles using a variety of structured and unstructured features and is aided by cross-language links and features in Wikipedia. Adding multilingual features helps boost classification accuracy and is shown to effectively classify multilingual pages in a language independent way. Classification is done using Support Vectors Machine (SVM) classifier at first, and then the threshold of SVM is adjusted in order to improve the recall scores of classification. Threshold adjustment is performed using beta-gamma threshold adjustment algorithm which is a post learning step that shifts the hyperplane of SVM. This approach boosted recall with minimal effect on precision.
Embed
Wikipedia Quality
Saleh, Iman; Darwish, Kareem; Fahmy, Aly A.. (2010). "[[Classifying Wikipedia Articles into Ne's Using Svm's with Threshold Adjustment]]". Association for Computational Linguistics.
English Wikipedia
{{cite journal |last1=Saleh |first1=Iman |last2=Darwish |first2=Kareem |last3=Fahmy |first3=Aly A. |title=Classifying Wikipedia Articles into Ne's Using Svm's with Threshold Adjustment |date=2010 |url=https://wikipediaquality.com/wiki/Classifying_Wikipedia_Articles_into_Ne's_Using_Svm's_with_Threshold_Adjustment |journal=Association for Computational Linguistics}}
HTML
Saleh, Iman; Darwish, Kareem; Fahmy, Aly A.. (2010). "<a href="https://wikipediaquality.com/wiki/Classifying_Wikipedia_Articles_into_Ne's_Using_Svm's_with_Threshold_Adjustment">Classifying Wikipedia Articles into Ne's Using Svm's with Threshold Adjustment</a>". Association for Computational Linguistics.