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Text Mining (TM) process, based on mining association rules using an algorithm, generates a significant amount of association rules. In this article, these are non-redundant as- sociation rules resulting from a mining process from a text corpus. We first propose to show the interest and usefulness of association rules filtred by a quality measure other than confidence, especially Entropic Implication Intensity (EII). Secondly, we present a new data structure "Con- textual meta-rules" to reduce the rule base by highlighting the new corresponding measures. We study our approachs in the context of Information Retrieval (IR) through the automatic Query Expansion (QE). Our experiments was conducted on English collection of CLEF 2003. The results showed a significant improvement.

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