Investigation of the Morphological and Root Structure of the Arabic Language Structure Using Python

Abstract

In the last years, scientists in computer and software have made successful studies in almost every field. Especially in the classification and comparison of optimization algorithms, metaheuristic optimization techniques can be generally classified as population-based and trajectory-based methods. These algorithms are quite suitable for high-dimensional problems and exhibit strong discovery capabilities. The algorithms use a single solution and iteratively improve it based on mathematical models or heuristics. Arabic is a language of derivation with a very rich derivational morphology, with virtually all words originating from roots through patterns. It is a language with numerous inflections and a complicated morphological structure. Arabic plurals used in modern Arabic are listed. Arabic is becoming a significant topic because of applications for information retrieval and natural language processing. There are two types of Arabic plurals: regular and irregular. The standard form also includes masculine and feminine versions. Stemming is a method used in information retrieval to differentiate between single and plural nouns by eliminating the corresponding affixes from words. In this study, we describe a technique that does not rely on stemming to recognize Arabic plurals. Based on the threefold relationship between word, root, and pattern, we utilize both the word and the pattern to find the correct root and the pattern used to define the type of plural. The algorithm has only been provided the fundamental patterns of plurals, whether regular or irregular; the remaining patterns are constructed programmatically to add the appropriate plugins for each type of plural. Finally, since irregular inclusion and humiliation are not governed by direct laws, we get positive outcomes in both cases. In the situation of irregular inclusion and humiliation, we are largely satisfied. Although generally faster, these methods may experience problems such as premature convergence or being stuck in local optima. Recent research has focused on improving existing algorithms by hybridization, parameter tuning, and incorporating additional mechanisms such as chaos theory and adversarial learning.