Browsing by Author "MAOUCHE Farah Hiba"

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Etude Comparative Des Racinisateurs Arabes.
(university of bordj bou arreridj, 2024) MAOUCHE Farah Hiba; KHALFA Lynda
Arabic language has a complex morphological structure, which presents a unique challenge in natural language processing (NLP). The derivational system of Arabic is based on roots, which are frequently modified to create new words, employing an extensive set of Arabic morphemes affixes such as prefixes, suffixes and more. Stemming is a fundamental task in text processing, plays a crucial role in information retrieval (IR) and text analysis, it reduces words to their basic or root form, facilitating text normalization for easier processing. However, no stemming algorithm for this language is perfect. In this work, we are going to focus on comparing and evaluating the performance of several Arabic stemmers namely, ISRI, Tashaphyne and Snowball. We intend to assess their performance on two distinct datasets using advanced techniques such as neural networks and machine learning classifiers. Additionally, we aim to determine which combination of stemmer and classifier yields the best results, providing invaluable insights for Arabic text processing applications