An algorithm that bootstraps the acquisition of large dictionaries of entity types (names) and pattern types from a few seeds and a large unannotated corpora is presented. The algorithm iteratively builds a bigraph of entities and collocated patterns by querying the text. Several classes simultaneously compete to label the entity types. Different experiments have been carried to acquire resources from a 1GB corpus of Spanish news. The usefulness of the acquired list of entity types for the task of Name Classification has also been evaluated with good results for a weakly supervised method.