A generalized joint inference approach for citation matching

Liao, Zhihua and Zhang, Zili 2008, A generalized joint inference approach for citation matching, Lecture notes in computer science, vol. 5360, pp. 601-607, doi: 10.1007/978-3-540-89378-3.

Attached Files
Name Description MIMEType Size Downloads

Title A generalized joint inference approach for citation matching
Author(s) Liao, Zhihua
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name Lecture notes in computer science
Volume number 5360
Start page 601
End page 607
Total pages 7
Publisher Springer
Place of publication Berlin, Germany
Publication date 2008
ISSN 0302-9743
Summary Citation matching is the problem of extracting bibliographic records from citation lists in technical papers, and merging records that represent the same publication. Generally, there are three types of data- sets in citation matching, i.e., sparse, dense and hybrid types. Typical approaches for citation matching are Joint Segmentation (Jnt-Seg) and Joint Segmentation Entity Resolution (Jnt-Seg-ER). Jnt-Seg method is effective at processing sparse type datasets, but often produces many errors when applied to dense type datasets. On the contrary, Jnt-Seg-ER method is good at dealing with dense type datasets, but insufficient when sparse type datasets are presented. In this paper we propose an alternative joint inference approach–Generalized Joint Segmentation (Generalized-Jnt-Seg). It can effectively deal with the situation when the dataset type is unknown. Especially, in hybrid type datasets analysis there is often no a priori information for choosing Jnt-Seg method or Jnt-Seg-ER method to process segmentation and entity resolution. Both methods may produce many errors. Fortunately, our method can effectively avoid error of segmentation and produce well field boundaries. Experimental results on both types of citation datasets show that our method outperforms many alternative approaches for citation matching.
Language eng
DOI 10.1007/978-3-540-89378-3
Field of Research 080105 Expert Systems
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2008, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017963

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 974 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Fri, 14 Aug 2009, 13:59:09 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.