An Empirical Study on the Feature’s Type Effect on the Automatic Classification of Arabic Documents

Arabic text classification methods: Systematic literature review of primary studies

“…TC algorithms require that text features are formatted before they can be interpreted by the specified classifier, this process is also referred to as term weighting because each term is entered together with a weight value. Included papers show the most used technique is the Term Frequency-Inverse Document Frequency (TF-IDF) as in [27,32,37,40,43,45,48,51,53,55,57,58,[60][61][62]67]. It is a statistical method to indicate the significance of a word within a given corpus.…”

Toward a Complex System for Context Discovery to Index Arabic Documents

“…Raheel et al [6] combined the Boosting method and the decision tree as a hybrid classifier. They used lemmatisation as a method of extracting the characteristics, and the TFIDF for the weighting.…”

Features based approach for indexation and representation of unstructured Arabic documents

“…Raheel et al [3] have shown in a comparative study the influence of the choice of entities representing a document, on manipulating the performance of classifiers. They selected as descriptors, words in their original form, lemmas, roots, and the ngrams.…”

“…Document indexing involves extracting keywords that best represent a document. In spite of the essential role of this phase in the next step of the natural language processing process, few are the works identified at this level [1][2] [3].…”