Abstract
A number of algorithms and approaches have been proposed towards the problem of scanning and digitizing research papers. We can classify work done in the past into three major approaches: regular expression based heuristics, learning based algorithm and knowledge based systems. Our findings point to the inadequacy of existing open-source solutions such as Paracite for papers with “micro-citations” in various European Languages. This paper describes the work done as part of the Google Summer of Code 2008 using a combination of regular-expression based heuristics and knowledge-based systems to develop a system which matches inline citations to their corresponding bibliographic references and identifies and extracts metadata from references. The description, implementation and results of our approach have been presented here. Our approach enhances the accuracy and provides better recognition rates.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jewel, M.: Paracite (2003), http://paracite.eprints.org/developers
Giuffrida, G., Shek, E.C., Yang, J.: Knowledge-based metadata extraction from PostScript files. In: DL 2000: Proceedings of the fifth ACM conference on Digital libraries, pp. 77–84. ACM Press, New York (2000)
Powley, B., Dale, R.: Evidence-based information extraction for high accuracy citation and author name identification. In: Proceedings of RIAO 2007: The 8th Conference on Large-Scale Semantic Access to Content, Pittsburgh, Pa., USA (2007)
Sautter, G., Böhm, K., Agosti, D.: A combining approach to find all taxon names (FAT). Biodiv. Inf. 3, 46–58 (2006)
Sautter, G., Böhm, K., Agosti, D.: A Quantitative Comparison of XML Schemas for Taxonomic. Biodiversity Informatics (2007)
McCallum, A., Nigam, K., Ungar, L.H.: Efficient clustering of high-dimensional data sets with application to reference matching. In: Knowledge Discovery and Data Mining, pp. 169–178 (2000)
Hetzner, E.: A simple method for citation metadata extraction using hidden markov models. In: Proceedings of the 8th ACM/IEEE-CS joint conference on Digital Libraries (2008)
Takasu: Bibliographic Attribute Extraction from Erroneous References Based on a Statistical Model. In: Proceedings of Joint Conference on Digital Libraries (2003)
Huang, I.A., Jan-Ming, H., Kao, H.Y., Lin, S.: Extracting citation metadata from online publication lists using BLAST. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS, vol. 3056, pp. 539–548. Springer, Heidelberg (2004)
Matt, E.D., Winkels, R., Van Engers, T.: Automated Detection of Reference Structures in Law. In: Proceedings of the Conference at University Pantheon, Assas, Paris II France, pp. 41–50 (2006)
Sautter, G., Agosti, D., Böhm, K.: Semi-Automated XML Markup of Biosystematics Legacy Literature with the GoldenGATE Editor. In: Proceedings of PSB, Wailea, HI USA (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gupta, D., Morris, B., Catapano, T., Sautter, G. (2009). A New Approach towards Bibliographic Reference Identification, Parsing and Inline Citation Matching. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-03547-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03546-3
Online ISBN: 978-3-642-03547-0
eBook Packages: Computer ScienceComputer Science (R0)