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Extraction and Characterization of Citations in Scientific Papers

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)

Abstract

We propose a hybrid method for the extraction and characterization of citations in scientific papers using machine learning combined with rule-based approaches. Our protocol consists of the extraction of metadata, bibliography parsing, section titles processing, and find-grained semantic annotation on the sentence level of texts. This allows us to generate Linked Open Data from a set of research papers in XML.

Keywords

Semantic annotation Citation acts CRF RDF graphs Linked Open Data Bibliography parsing 

Notes

Acknowledgments

We thank Angelo Di Iorio at the Department of Computer Science and Engineering (DISI) of the University of Bologna for providing the gold standard and the evaluation.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CIRSTUniversité du Québec à MontréalMontrealCanada

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