BioKeySpotter: An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection

Min Song, Prat Tanapaisankit

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Extracting keyphrases from full-text is a daunting task in that many different concepts and themes are intertwined and extensive term variations exist in full-text. In this chapter, we proposes a novel unsupervised keyphrase extraction system, BioKeySpotter, which incorporates lexical syntactic features to weigh candidate keyphrases. The main contribution of our study is that BioKeySpotter is an innovative approach for combining Natural Language Processing (NLP), information extraction, and integration techniques into extracting keyphrases from full-text. The results of the experiment demonstrate that BioKeySpotter generates a higher performance, in terms of accuracy, compared to other supervised learning algorithms.

Original languageEnglish
Title of host publicationData Mining
Subtitle of host publicationFoundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications
EditorsDawn Holmes, Lakhmi Jain
Pages19-27
Number of pages9
DOIs
Publication statusPublished - 2012 Dec 1

Publication series

NameIntelligent Systems Reference Library
Volume25
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

Fingerprint

Supervised learning
Syntactics
Learning algorithms
information processing
candidacy
Experiments
experiment
language
learning
performance
Information integration
Information extraction
Learning algorithm
Natural language processing
Experiment
High performance

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Information Systems and Management
  • Library and Information Sciences

Cite this

Song, M., & Tanapaisankit, P. (2012). BioKeySpotter: An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection. In D. Holmes, & L. Jain (Eds.), Data Mining: Foundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications (pp. 19-27). (Intelligent Systems Reference Library; Vol. 25). https://doi.org/10.1007/978-3-642-23151-3_3
Song, Min ; Tanapaisankit, Prat. / BioKeySpotter : An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection. Data Mining: Foundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications. editor / Dawn Holmes ; Lakhmi Jain. 2012. pp. 19-27 (Intelligent Systems Reference Library).
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Song, M & Tanapaisankit, P 2012, BioKeySpotter: An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection. in D Holmes & L Jain (eds), Data Mining: Foundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications. Intelligent Systems Reference Library, vol. 25, pp. 19-27. https://doi.org/10.1007/978-3-642-23151-3_3

BioKeySpotter : An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection. / Song, Min; Tanapaisankit, Prat.

Data Mining: Foundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications. ed. / Dawn Holmes; Lakhmi Jain. 2012. p. 19-27 (Intelligent Systems Reference Library; Vol. 25).

Research output: Chapter in Book/Report/Conference proceedingChapter

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AB - Extracting keyphrases from full-text is a daunting task in that many different concepts and themes are intertwined and extensive term variations exist in full-text. In this chapter, we proposes a novel unsupervised keyphrase extraction system, BioKeySpotter, which incorporates lexical syntactic features to weigh candidate keyphrases. The main contribution of our study is that BioKeySpotter is an innovative approach for combining Natural Language Processing (NLP), information extraction, and integration techniques into extracting keyphrases from full-text. The results of the experiment demonstrate that BioKeySpotter generates a higher performance, in terms of accuracy, compared to other supervised learning algorithms.

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Song M, Tanapaisankit P. BioKeySpotter: An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection. In Holmes D, Jain L, editors, Data Mining: Foundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications. 2012. p. 19-27. (Intelligent Systems Reference Library). https://doi.org/10.1007/978-3-642-23151-3_3