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Semelhante a Analysing Entity Type Variation across Biomedical Subdomains
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Analysing Entity Type Variation across Biomedical Subdomains
- 1. Analysing Entity Type Variation across Biomedical Subdomains Claudiu Mihăilă, Riza Theresa Batista-Navarro, Sophia Ananiadou Claudiu Mihăilă National Centre for Text Mining School of Computer Science University of Manchester 26 May 2012
- 2. BioTxtM 2012 Introduction • Named entities o Atomic elements, classified into various categories (protein, gene, disease, treatment, metabolite etc.) Theme Organism Theme Organism Pro Pro Pro Transcription +Reg Pro In contrast to the phenotype of the pta ackA double mutant, pbgP transcription was reduced in the pmrD mutant. 2
- 4. BioTxtM 2012 Methodology • Full-text open-access journal articles from UKPMC • 20 subdomains 400 single broad-subject-termed articles Allergy & Communicable Biology Cell Biology Critical Care Immunology Diseases Health Environmental Medical Genetics Services Medicine Health Informatics Research Microbiology Neoplasms Neurology Pharmacology Physiology Pulmonary Tropical Public Health Rheumatology Virology Medicine Medicine 4
- 5. BioTxtM 2012 Methodology • NE source: ASilver = AUKPMC AOscar ANeMine Corpus Annotation Allergy & UKPMC Communicable Critical Care Biology Cell Biology Critical Care Immunology Diseases Health Environmental Medical Medicine Genetics Services Medicine Health Informatics Research OSCAR Physiology Microbiology Neoplasms Neurology Pharmacology Physiology Pulmonary Tropical Virology Public Health Rheumatology NeMine Virology Medicine Medicine 5
- 6. BioTxtM 2012 Methodology NeMine UKPMC Gene Gene Protein Protein Disease Disease Drug Drug Metabolite Metabolite Bacteria Gene|Protein Diagnostic process General phenomenon Silver Indicator Annotation Natural phenomenon OSCAR Organ Chemical molecule Pathologic function Chemical adjective Symptom Enzyme Therapeutic process Reaction 6
- 7. BioTxtM 2012 Methodology • Feature vectors Document d Document d Enzyme 2 Enzyme 0.45% Chemical molecule 71 Chemical molecule 14.85% Disease 8 Disease 1.67% Drug 12 Drug 2.51% Gene 15 Gene 3.13% Gene|Protein 155 Gene|Protein 3.24% Metabolite 3 Metabolite 0.62% Protein 188 Protein 39.33% Reaction 24 Reaction 5.02% 7
- 10. BioTxtM 2012 Methodology • Chi-squared statistics 10
- 11. BioTxtM 2012 Methodology • Frobenius norm 1247.0725 11
- 12. BioTxtM 2012 Feature evaluation • Good features for o Cell Biology o Pharmacology o Health Sciences o Public Health • Not-so-good features for o Medical Informatics o Medicine o Microbiology o Neoplasms o Neurology Frobenius norm of 2 vectors for each pair. 12
- 13. BioTxtM 2012 Feature evaluation • Mean Chi-Squared for every feature over all pairs 13
- 14. BioTxtM 2012 Classifier selection Classifier Top result count J48 0 0% JRip 4 2.10% Logistic 2 1.05% Random Tree 0 0% Random Forest 86 45.26% SMO 0 0% J48 6 3.15% JRip 7 3.68% Decision Stump 16 8.42% AdaBoost Logistic 0 0% Random Tree 0 0% Random Forest 68 35.78% Random Forest F-score for each5.26% SMO 1 pair. 14
- 15. BioTxtM 2012 Classifier evaluation • Dissimilar subdomains o Cell Biology o Pharmacology o Health Sciences o Public Health • Similar subdomains o Medical Informatics o Medicine o Microbiology o Neoplasms o Neurology Random Forest F-score for each pair. 15
- 16. BioTxtM 2012 Conclusions • To remember o Significant semantic variation of biomedical sublanguages o Distinguishable bio-subdomains using only NE types o Caution needed when adapting NLP tools to subdomains • To do o Extension to bio-events o Combination with lexical, syntactical, discourse features o Extension to other domains 16
- 17. BioTxtM 2012 Thank you! http://misteringo.deviantart.com/art/Bunnies-Scream-Again-79745974 17