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Automatic citation metadata extraction using hidden markov models

  • Ni Zhen*
  • , Xu Hong
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automatic citation metadata extraction is an important aspect of digital library development. The previous methods which using hidden markov models to extract citation metadata mostly need label many training data manually. To save the high cost of labeling training data manually, this paper describes a method for citation metadata extraction using hidden markov models. This method use unlabeled data (plain texts which we want to extract metadata) as training data. The results of experiment show that our method has good performance in precision and recall.

Original languageEnglish
Title of host publication2009 1st International Conference on Information Science and Engineering, ICISE 2009
Pages802-805
Number of pages4
DOIs
StatePublished - 2009
Event1st International Conference on Information Science and Engineering, ICISE2009 - Nanjing, China
Duration: 26 Dec 200928 Dec 2009

Publication series

Name2009 1st International Conference on Information Science and Engineering, ICISE 2009

Conference

Conference1st International Conference on Information Science and Engineering, ICISE2009
Country/TerritoryChina
CityNanjing
Period26/12/0928/12/09

Keywords

  • Hidden markov model
  • Metadata extraction

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