Arabic Stop Words for Information Retrieval Systems

Abstract

This paper explores the classification, analysis, and implications of Arabic stop words in the context of natural language processing (NLP) and information retrieval systems. Arabic, with its complex morphological and syntactic structures, poses unique challenges for automated data processing. Stop words, such as prepositions, adverbs, conjunctions, and interjections, often comprise a significant portion of text but carry minimal semantic value. Their removal is essential for improving the efficiency and accuracy of text analysis tools. The study employs both manual selection and statistical analysis to develop a comprehensive stop word list, focusing on Quranic text as a reference corpus. By integrating linguistic insights and computational techniques, the paper highlights the importance of stop words in stemming algorithms and indexing processes. The findings emphasize the potential for improved retrieval performance and reduced computational overhead through effective stop word handling. This work contributes to advancing Arabic text processing and serves as a foundation for future research in this field.