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%.