  In Information Retrieval System (IRS), the Automatic Relevance Feedback (ARF)
is a query reformulation technique that modifies the initial one without the
user intervention. It is applied mainly through the addition of terms coming
from the external resources such as the ontologies and or the results of the
current research. In this context we are mainly interested in the local
analysis technique for the ARF in ad-hoc IRS on Arabic documents. In this
article, we have examined the impact of the variation of the two parameters
implied 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. The
experimentation, carried out on an Arabic corpus text, enables us to deduce
that there are queries which are not easily improvable with the query
reformulation. In addition, the success of the ARF is due mainly to the
selection of a sufficient number of documents D and to the extraction of a very
reduced set of relevant terms T for retrieval.
