  In this work, we compare two simple methods of tagging scientific
publications with labels reflecting their content. As a first source of labels
Wikipedia is employed, second label set is constructed from the noun phrases
occurring in the analyzed corpus. We examine the statistical properties and the
effectiveness of both approaches on the dataset consisting of abstracts from
0.7 million of scientific documents deposited in the ArXiv preprint collection.
We believe that obtained tags can be later on applied as useful document
features in various machine learning tasks (document similarity, clustering,
topic modelling, etc.).
