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 

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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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