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.

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Title A generalized joint inference approach for citation matching
Author(s) Liao, Zhihua
Zhang, Zili
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
1611-3349
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
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

Document type: Journal Article
Collection: School of Engineering and Information Technology
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