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In Information Retrieval System (IRS), the Automatic Relevance Feedback (ARF)is a query reformulation technique that modifies the initial one without theuser intervention. It is applied mainly through the addition of terms comingfrom the external resources such as the ontologies and or the results of thecurrent research. In this context we are mainly interested in the localanalysis technique for the ARF in ad-hoc IRS on Arabic documents. In thisarticle, we have examined the impact of the variation of the two parametersimplied in this technique, that is to say, the number of the documents{\guillemotleft}D{\guillemotright} and the number of terms{\guillemotleft}T{\guillemotright}, on an Arabic IRS performance. Theexperimentation, carried out on an Arabic corpus text, enables us to deducethat there are queries which are not easily improvable with the queryreformulation. In addition, the success of the ARF is due mainly to theselection of a sufficient number of documents D and to the extraction of a veryreduced set of relevant terms T for retrieval.

The Number of Terms and Documents for Pseudo-Relevant Feedback for Ad-hoc Information Retrieval