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)


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
Number of pages9
Publication statusPublished - 2012

Publication series

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

All Science Journal Classification (ASJC) codes

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


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