Combining information extraction with genetic algorithms for text mining

John Atkinson, Christopher Stuart Mellish, Stuart Aitken

Research output: Contribution to journalArticle

42 Citations (Scopus)

Abstract

An evolutionary approach that combines information extraction technology and genetic algorithms can produce a new, integrated model for text mining. Text mining discovers unseen patterns in textual databases. We've brought together the benefits of GAs for data mining and IE technology to propose a new approach for high-level knowledge discovery. Unlike previous KDT approaches, our model doesn't rely on external resources or conceptual descriptions. Instead, it performs the discovery using only information from the original corpus of text documents and from training data computed from them. The GA that produces the hypotheses is strongly guided by semantic constraints, which means that several specifically defined metrics evaluate the quality and plausibility.
Original languageEnglish
Pages (from-to)22-30
Number of pages9
JournalIEEE Intelligent Systems
Volume19
Issue number3
DOIs
Publication statusPublished - May 2004

Keywords

  • data mining
  • genetic algorithms
  • knowledge discovery from texts (KDTs)
  • multiobjective optimization
  • semantic analysis
  • text mining
  • information extraction
  • textual database
  • information analysis
  • natural languages
  • pattern analysis
  • performance analysis
  • robustness

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