%0 Journal Article
%T Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain
%A Andr¨¦ SANTOS
%A Regina NOGUEIRA
%A An¨¢lia LOUREN£¿O
%J ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal
%D 2013
%I Universidad de Salamanca
%X Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents.
%K Text mining
%K Biotechnology applications
%K Procedure optimization
%U http://campus.usal.es/~revistas_trabajo/index.php/2255-2863/article/view/10085