One of the potentially most relevant pieces of metadata for filtering studies in environmental science is the geographic region in which the study took place (the "study region"). In this paper, we apply support vector machines to the automatic classification of study region in a dataset of titles and abstracts from environmental science literature, using features including frequency distributions of resolved toponyms and a bag of word unigrams. We found that we can determine the study region with high accuracy, with the strongest classifier achieving an accuracy of 0.892 combining toponym resolution from DBpedia and GeoNames with the bag-of-toponyms features.
As a specific instance of toponym resolution over environmental sciences data, REF proposed a method for predicting the "study region" of a published abstract, based on text categorisation techniques using features including frequency distributions of resolved toponyms and a bag of word unigrams.
Classification of Study Region in Environmental Science Abstracts
ALTA
Environmental Science Computer Science