Free text botanical descriptions contained in printed floras can provide a wealth of valuable scientific information. In spite of this richness, these texts have seldom been analyzed on a large scale using NLP techniques. To fill this gap, we describe how we managed to extract a set of terminological resources by parsing a large corpus of botanical texts. The tools and techniques used are presented as well as the rationale for favoring a deep parsing approach coupled with error mining methods over a simple pattern matching approach.