Elnahaas, A., Elfishawy, N., Elsayed, M., Atteya, G., Tolba, M. (2018). Query Expansion for Arabic Information Retrieval Model: Performance Analysis and Modification. The Egyptian Journal of Language Engineering, 5(1), 11-24. doi: 10.21608/ejle.2018.59298
Ayat Elnahaas; Nawal Elfishawy; Mohamed Nour Elsayed; Gamal M. Atteya; Maha Saad Tolba. "Query Expansion for Arabic Information Retrieval Model: Performance Analysis and Modification". The Egyptian Journal of Language Engineering, 5, 1, 2018, 11-24. doi: 10.21608/ejle.2018.59298
Elnahaas, A., Elfishawy, N., Elsayed, M., Atteya, G., Tolba, M. (2018). 'Query Expansion for Arabic Information Retrieval Model: Performance Analysis and Modification', The Egyptian Journal of Language Engineering, 5(1), pp. 11-24. doi: 10.21608/ejle.2018.59298
Elnahaas, A., Elfishawy, N., Elsayed, M., Atteya, G., Tolba, M. Query Expansion for Arabic Information Retrieval Model: Performance Analysis and Modification. The Egyptian Journal of Language Engineering, 2018; 5(1): 11-24. doi: 10.21608/ejle.2018.59298
Query Expansion for Arabic Information Retrieval Model: Performance Analysis and Modification
1Department of Research Informatics, Electronics Research Institute, Cairo, Egypt
2Computer Science and Engineering Dept., Faculty of Electronic Eng.,Menoufia University
3Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Egypt
4Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University
Abstract
Information retrieval aims to find all relevant documents responding to a query from textual data. A good information retrieval system should retrieve only those documents that satisfy the user query. Although several models were developed, most of Arabic information retrieval models do not satisfy the user needs. This is because the Arabic language is more powerful and has complex morphology as well as high polysemy. This paper first investigates the most recent Arabic information retrieval model and then presents two different approaches to enhance the effectiveness of the adopted model. The main idea of the proposed approaches is to modify and/or expand the user query. The first approach expands user query by using semantics of words according to an Arabic dictionary. The second approach modifies and/or expands user query by adding some useful information from the pseudo relevance feedback. In other words, the query is modified by selecting relevant textual keywords for expanding the query and weeding out the non-related textual words. The adopted retrieval model and the two proposed approaches are implemented, tested, compared, and evaluated considering Arabic document collection. The obtained results show that the proposed approaches enhance the effectiveness of the Arabic information retrieval model by about 15% to 35%.
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