After a recent breakthrough in the early 90's, text analysis is acknowledged as one of the promising ways to rapidly build better grounded semantic resources such as terminologies and ontologies. This domain has recently undergone significant evolutions with a massive reference to machine learning algorithms and information extraction techniques together with linguistic- and statistic-based natural language processing. This position paper promotes three main ideas: (i) that highly domain-specific or task-specific, even idiosyncratic ontologies, are very useful, especially when they are linked to broader consensual schemes and they can be built with reasonable effort; (ii) that corpus-based ontologies can capture the perspective of a domain; and (iii) that supervised ontology learning from text makes feasible the development of specialized ontologies adapted for specific uses. We propose the establishment of an inventory of tools for building ontologies from text, give a first classification of such tools, and present an initial review of some recent methods and tools.