New stemming for arabic text classification using feature selection and decision trees

@inproceedings{Bahassine2014NewSF,
  title={New stemming for arabic text classification using feature selection and decision trees},
  author={Said Bahassine and Morocco Bsaid and Mohamed Kissi EMMID and Abdellah Madani},
  year={2014}
}
In this paper we conduct a comparative study between two stemming algorithms: khoja stemmer and our new stemmer for Arabic text classification (categorization), using Chisquare statistics as feature selection and focusing on decision tree classifier. Evaluation used a corpus that consists of 5070 documents independently classified into six categories: sport, entertainment, business, middle east, switch and world, on WEKA toolkit. The recall measure is used to compare the performance of these… CONTINUE READING

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