The text mining tools proposed in this paper extract association rules from a set of specialized and homogeneous texts (corpus).
This tool is built in several steps and, at each of them, the expert plays a fundamental role.
The first step extracts the terms from the corpus, and clusters them in classes by semantic similarity, associating each class to a concept meaningful to a field expert.
Using the knowledge thus obtained, the corpus generates a table of concept frequencies in the texts.
Next, we discretize the values of this table, and finally we are able to extract association rules among the concept occurrences.
