{'fraction_non_alphanumeric': 0.02109704641350211, 'fraction_numerical': 0.0, 'mean_word_length': 5.785714285714286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Arabic Text Classification Using N-Gram Frequency Statistics A Comparative Study

This paper presents the results of classifying Arabic text documents using the N-gram frequency statistics technique employing a dissimilarity measure called the “Manhattan distance”, and Dice’s measure of similarity. The Dice measure was used for comparison purposes. Results show that N-gram text classification using the Dice measure outperforms classification using the Manhattan measure.