CERMINE is a comprehensive open source system for extracting structured metadata from scientific articles in a born-digital form. Among other information, CERMINE is able to extract authors and affiliations of a given publication, establish relations between them and present extracted metadata in a structured, machine-readable form. Affiliations extraction is based on a modular workflow and utilizes supervised machine learning as well as heuristic-based techniques. According to the evaluation we performed, the algorithm achieved good results both in affiliations extraction (84.3% F1) and affiliations parsing (92.1% accuracy) tasks. In this paper we outline the overall affiliations extraction work flow and provide details about individual steps' implementations. We also compare our approach to similar solutions, thoroughly describe the evaluation methodology and report its results. The CERMINE system, including the entire affiliations extraction and parsing functionality, is available under an open-source licence.
['Dominika Tkaczyk', 'Bartosz Tarnawski', 'Lukasz Bolikowski']
Structured Affiliations Extraction from Scientific Literature