Introduction to text mining
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Introduction to text mining

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Introduction to text mining Presentation Transcript

  • 1. Introduction to text mining Lars Juhl Jensen >10 km
  • 2. exponential growth
  • 3.  
  • 4. ~45 seconds per paper
  • 5. text mining
  • 6. information retrieval
  • 7. find the relevant papers
  • 8. user-specified query
  • 9. “ yeast AND cell cycle”
  • 10.  
  • 11. entity recognition
  • 12. identify the concepts
  • 13. comprehensive lexicon
  • 14. orthographic variation
  • 15. “ black list”
  • 16. Reflect
  • 17. augmented browsing
  • 18. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology , 2009
  • 19. used by publishers
  • 20.  
  • 21. information extraction
  • 22. formalize the facts
  • 23. co-mentioning
  • 24. NLP Natural Language Processing
  • 25.
    • Gene and protein names
    • Cue words for entity recognition
    • Verbs for relation extraction
    • [ nxexpr T he expression of [ nxgene the cytochrome genes [ nxpg CYC1 and CYC7 ]]] is controlled by [ nxpg HAP1 ]
  • 26. molecular networks
  • 27.  
  • 28. information on side effects
  • 29. Campillos & Kuhn et al., Science , 2008
  • 30. Acknowledgments
      • Sean O’Donoghue
      • Sune Frankild
      • Heiko Horn
      • Evangelos Pafilis
      • Michael Kuhn
      • Reinhardt Schneider