File(s) under permanent embargo
A generalized joint inference approach for citation matching
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.
History
Journal
Lecture notes in computer scienceVolume
5360Pagination
601 - 607Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2008, Springer-VerlagUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC