Automatic Climate Classification of Environmental Science Literature

Abstract

Climate type is one of the potentially most relevant pieces of metadata for identifying studies in evidence-based environmental management. In this paper, we propose a method for automatically predicting the climate type in environmental science literature using NLP techniques, relative to a pre-existing set of climate type categories. Our main approaches combine toponym detection and resolution using two different resources with support vector machines. The results show great promise, but also further challenges, for using NLP to extract information from the vast and rapidly growing collection of environmental sciences literature.