One of the most important issues when constructing an Information Extraction System is how to obtain the knowledge needed for identifying relevant information in a document. A manual approach not only is an expensive solution but also has a negative effect on the portability of the system across domains. To automatize the knowledge acquisition process may partially solve this problem even if a human expert takes part in it only for specific tasks. This work presents a methodology to automatically learn information extraction patterns from unrestricted text corpus representative of the domain. The methodology includes different steps from which we stress the specific pattern generalization process to obtain hight coverage patterns while maintaining the relevance of the extracted information.