Due to the nature of textual data the application of association rule mining in text corpora has attracted the focus of the research scientific community for years. In this paper we demonstrate a system that can efficiently mine association rules from text. The system annotates terms using several annotators, and extracts text association rules between terms or categories of terms. An additional contribution of this work is the inclusion of novel unsupervised evaluation measures for weighting and ranking the importance of the text rules. We demonstrate the functionalities of our system with two text collections, a set of Wikileaks documents, and one from TREC-7.