research-article

Web-based citation parsing, correction and augmentation

Authors:

Ying LiuAuthors Info & Claims

JCDL '12: Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries

Pages 295 - 304

https://doi.org/10.1145/2232817.2232872

Published: 10 June 2012 Publication History

Abstract

Considering the tremendous value of citation metadata, many methods have been proposed to automate Citation Metadata Extraction (CME). The existing methods primarily rely on the content analysis of citation text. However, the results from such content-based methods are often unreliable. Moreover, the extracted citation metadata is only a small part of the relevant metadata that spreads across the Internet. As opposed to the content-based CME methods, this paper proposes a Web-based CME approach and a citation enriching system, called as BibAll, which is capable of correcting the parsing results of content-based CME methods and augmenting citation metadata by leveraging relevant bibliographic data from digital repositories and cited-by publications on the Web. BibAll consists of four main components: citation parsing, Web-based bibliographic data retrieval, irrelevant bibliographic data filtering, and relevant bibliographic data integration. The system has been tested on the publicly available FLUX-CIM dataset. Experimental results show that BibAll significantly improves the citation parsing accuracy and augments the metadata of the original citation.

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Cited By

Jain VBaliyan NKumar S(2023)Machine Learning Approaches for Entity Extraction from Citation StringsDecision Intelligence10.1007/978-981-99-5997-6_25(287-297)Online publication date: 25-Nov-2023
https://doi.org/10.1007/978-981-99-5997-6_25
Cuong NChandrasekaran MKan MLee WBogen PAllard SMercer HBeck MCunningham SGoh DHenry G(2015)Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFsProceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries10.1145/2756406.2756946(61-64)Online publication date: 21-Jun-2015
https://dl.acm.org/doi/10.1145/2756406.2756946
Lindner S(2015)Two-Tier Machine Learning Using Conditional Random Fields with ConstraintsKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-662-46549-3_6(80-95)Online publication date: 25-Apr-2015
https://doi.org/10.1007/978-3-662-46549-3_6
Show More Cited By

Index Terms

Web-based citation parsing, correction and augmentation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

JCDL '12: Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries

June 2012

458 pages

ISBN:9781450311540

DOI:10.1145/2232817

General Chairs:
Karim B. Boughida
The George Washington University, USA
,
Barrie Howard
The Library of Congress, USA
,
Program Chairs:
Michael L. Nelson
Old Dominion University, USA
,
Herbert Van de Sompel
Los Alamos National Laboratory, USA
,
Ingeborg Sølvberg
Norwegian University of Science & Technology, Norway

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 June 2012

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JCDL '12: 12th ACM/IEEE-CS Joint Conference on Digital Libraries

June 10 - 14, 2012

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Cited By

Jain VBaliyan NKumar S(2023)Machine Learning Approaches for Entity Extraction from Citation StringsDecision Intelligence10.1007/978-981-99-5997-6_25(287-297)Online publication date: 25-Nov-2023
https://doi.org/10.1007/978-981-99-5997-6_25
Cuong NChandrasekaran MKan MLee WBogen PAllard SMercer HBeck MCunningham SGoh DHenry G(2015)Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFsProceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries10.1145/2756406.2756946(61-64)Online publication date: 21-Jun-2015
https://dl.acm.org/doi/10.1145/2756406.2756946
Lindner S(2015)Two-Tier Machine Learning Using Conditional Random Fields with ConstraintsKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-662-46549-3_6(80-95)Online publication date: 25-Apr-2015
https://doi.org/10.1007/978-3-662-46549-3_6
Williams KLi LKhabsa MWu JShih PGiles C(2014)A Web Service for Scholarly Big Data Information ExtractionProceedings of the 2014 IEEE International Conference on Web Services10.1109/ICWS.2014.27(105-112)Online publication date: 27-Jun-2014
https://dl.acm.org/doi/10.1109/ICWS.2014.27
Hsiao WChang TThomas E(2014)Extracting bibliographical data for PDF documents with HMM and external resourcesProgram10.1108/PROG-12-2011-005948:3(293-313)Online publication date: Jul-2014
https://doi.org/10.1108/PROG-12-2011-0059
Do HChandrasekaran MCho PKan MDownie JMcDonald RCole TSanderson RShipman F(2013)Extracting and matching authors and affiliations in scholarly documentsProceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries10.1145/2467696.2467703(219-228)Online publication date: 22-Jul-2013
https://dl.acm.org/doi/10.1145/2467696.2467703

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