A large amount of quantitative data, related to experimental results, is reported in scientific documents in a free form of text. Each quantitative result is characterized by a numerical value often followed by a unit of measure. Extracting automatically quantitative data is a painstaking process because units suffer from different ways of writing within documents. In our paper, we propose to focus on the extraction and identification of the variant units, in order to enrich iteratively the terminological part of an Ontological and Terminological Resource (OTR) and in the end to allow the extraction of quantitative data. Focusing on unit extraction involves two main steps. Since we work on unstructured documents, units are completely drowned in textual information. In the first step, our method aims at handling the crucial time-consuming process of unit location using supervised learning methods. Once the units have been located in the text, the second step of our method consists in extracting and identifying candidate units in order to enrich the OTR. The extracted candidates are compared to units already known in the OTR using a new string distance measure to validate whether or not they are relevant variants. We have made concluding experiments on our two-step method on a set of more than 35000 sentences.