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A Probabilistic Term Variant Generator for Biomedical Terms

A Probabilistic Term Variant Generator for Biomedical Terms. Yoshimasa Tsuruoka and Jun ’ ichi Tsujii CREST, JST The University of Tokyo. Outline. Probabilistic Term Variant Generator Generation Algorithm Application: Dictionary expansion. Background.

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A Probabilistic Term Variant Generator for Biomedical Terms

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  1. A Probabilistic Term Variant Generator for Biomedical Terms Yoshimasa Tsuruoka and Jun’ichi Tsujii CREST, JST The University of Tokyo

  2. Outline • Probabilistic Term Variant Generator • Generation Algorithm • Application: Dictionary expansion

  3. Background • Information extraction from biomedical documents • Recognizing technical terms (e.g. DNA, protein names) We measured glucocorticoid receptors ( GR ) in mononuclear leukocytes ( MNL ) isolated…

  4. Technical Term Recognition • Machine learning based • Identifying the regions of terms ⇒　No ID information • Dictionary-based • Comparing the strings with each entry in the dictionary ⇒　ID information

  5. Problems of Dictionary-based approaches • Spelling variation degrades recall 　⇒ Approximate string searching • False positivesdegrade precision 　⇒ Filtering by machine learning

  6. Exact String Searching • Example • Text Phorbol myristate acetate induced Egr-1 mRNA… • Dictionary EGP EGR-1 EGR-1 binding protein : ⇒　Any of them does not match

  7. Edit Distance • Defines the distance of two strings by the sequence of three kinds of operations. • Substitution • Insertion • Deletion • Ex.)board　→　abord • Cost = 2 (delete `a’ and add `a’)

  8. Automatic Generation of Spelling Variants • Variant Generator NF-Kappa B (1.0) NF Kappa B (0.9) NF kappa B (0.6) NF kappaB (0.5) NFkappaB (0.3) : Generator NF-Kappa B Each generated variant is associated with its generation probability

  9. Generation Algorithm • Recursive generation P = P’ x Pop T cell (1.0) 0.5 0.2 T-cell (0.5) T cells (0.2) 0.2 T-cells (0.1)

  10. Collecting Examples of Spelling Variation • Abbreviation Extraction （Schwartz 2003） • Extracts short and long form pairs

  11. Learning Operation Rules • Operations for generating variants • Substitution • Deletion • Insertion • Context • Character-level context: preceding (following) two characters • Operation Probability

  12. Probabilistic Rules

  13. Example (1)

  14. Example (2)

  15. Example (3)

  16. Application:Dictionary Expansion • Expanding each entry in the dictionary • Threshold of Generation Probability: 0.1 • Max number of variants for each entry: 20

  17. Protein Name Recognition • Information Extraction • Longest match • GENIA corpus

  18. Results of Dictionary Expansion • a

  19. Conclusion • Probabilistic Variant Generator • Learning from actual examples • Dictionary expansion by the generator improves recall without the loss of precision.

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