Abstract
The extraction of individual reference strings from the reference section of scientific publications is an important step in the citation extraction pipeline. Current approaches divide this task into two steps by first detecting the reference section areas and then grouping the text lines in such areas into reference strings. We propose a classification model that considers every line in a publication as a potential part of a reference string. By applying line-based conditional random fields rather than constructing the graphical model based on individual words, dependencies and patterns that are typical in reference sections provide strong features while the overall complexity of the model is reduced. We evaluated our novel approach RefExt against various state-of-the-art tools (CERMINE, GROBID, and ParsCit) and a gold standard which consists of 100 German language full text publications from the social sciences. The evaluation demonstrates that we are able to outperform state-of-the-art tools which rely on the identification of reference section areas.
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Acknowledgements
This work has been funded by Deutsche Forschungsgemeinschaft (DFG) as part of the project “Extraction of Citations from PDF Documents (EXCITE)” under grant numbers MA 3964/8-1 and STA 572/14-1. We would like to thank Dominika Tkaczyk for her support regarding the CERMINE tool as well as Alexandra Bormann, Jan Hübner, and Daniel Kostić for contributing to the gold standard that was used in this research.
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Körner, M., Ghavimi, B., Mayr, P., Hartmann, H., Staab, S. (2017). Evaluating Reference String Extraction Using Line-Based Conditional Random Fields: A Case Study with German Language Publications. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_15
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DOI: https://doi.org/10.1007/978-3-319-67162-8_15
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