Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization

@article{Duwairi2007StemmingVL,
  title={Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization},
  author={Rehab Duwairi and Mohammed Al-Refai and Natheer Khasawneh},
  journal={2007 Innovations in Information Technologies (IIT)},
  year={2007},
  pages={446-450}
}
This paper compares and contrasts two feature selection techniques when applied to Arabic corpus; in particular; stemming, and light stemming were employed. With stemming, words are reduced to their stems. With light stemming, words are reduced to their light stems. Stemming is aggressive in the sense that it reduces words to their 3-letters roots. This affects the semantics as several words with different meanings might have the same root. Light stemming, by comparison, removes frequently used… CONTINUE READING
Highly Cited
This paper has 47 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 25 extracted citations

Improving Arabic document categorization: Introducing local stem

2010 10th International Conference on Intelligent Systems Design and Applications • 2010
View 8 Excerpts
Highly Influenced

A machine Learning approach for sentiment analysis in the standard or dialectal Arabic Facebook comments

2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) • 2017
View 1 Excerpt

JIDR: Towards building hybrid Arabic stemmer

2017 International Conference on Mathematics and Information Technology (ICMIT) • 2017
View 1 Excerpt

A combination of low-level light stemming and support vector machines for the classification of Arabic opinions

2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA) • 2016
View 1 Excerpt

Automatic summarization of the Arabic documents using NMF: A preliminary study

2016 11th International Conference on Computer Engineering & Systems (ICCES) • 2016
View 3 Excerpts

References

Publications referenced by this paper.
Showing 1-10 of 15 references

A New Approach for Extracting Arabic Roots

R Al Shalabi, G Kanaan, H. Al-Serhan
In the Proceedings of the International Arab Conference on Information Technology; • 2003
View 5 Excerpts
Highly Influenced

Active Learning of SVM and Decision Tree Classifiers for Text Categorization

B. Peter
Proceedings of the 4th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence; • 2006
View 1 Excerpt

Text Categorization

Encyclopedia of Database Technologies and Applications • 2005
View 1 Excerpt

Support Vector Machines for Text Categorization

Basu, A. Walters, M. C. Shepherd
Proceedings of the 36th Annual Hawaii International Conference; • 2003
View 1 Excerpt

Similar Papers

Loading similar papers…