Based on this count it will take the prober decision.

Based on this count it will take the prober decision. Finally, the stemmer tries to solve the problem of so called broken plural in Arabic, in which, a noun in pluraltakes another morphological form different from its initial form in singular. To do that, the stemmer keeps a table of patterns for all broken plural and their singular form, this table is shown in Table 3. Table 3 Singular and plural patterns.

The following (figure 5) is the result of the stemming phase in our project for the terms shown in figure 4. Figure 5: The stemmer output Feature extraction This phase starts by splitting the data set into training set and test set. The size of the test set is determined by a parameter "test_set_ratio". In this sub-phase, the most informative terms are extracted from documents. There are two main benefits from the feature extraction.

The first is that it reduces the number of dimensions(terms) and thus reduces the classifier complexity and processing requirements (Time, Memory, & Desk Space). The second is increasing the classification…