Soft Computing Approaches for tagging Arabic text

This work developed two-stage labeling models. The first stage is to process the text before execution by removing the suffixes attached to it. And then the second stage is to design models by applying mathematical models using Multilayer Perceptron (MPL), Full Recurrent Neural Network (FRNN), and Support Vector Machines (SVM). The current system helps classify words and allocate the correct parts of speech according to their wholesale position. To test the effectiveness of the proposed models using two different languages (Arabic and Hindi). The results showed the effectiveness of the proposed models is successfully solving the problem of clarification of words for the Arabic text. Also, compared to previous studies, the proposed models achieved high accuracy by classifying the parts of speech with an accuracy of up to (99%).