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Applying dependency parses and SRL: Subject and Generic Attribute Discovery

Applying dependency parses and SRL: Subject and Generic Attribute Discovery. Stephen Wu, Mayo Clinic SHARPn Summit 2012 June 11, 2012. Outline. Motivation and Role Generic Attribute Definition Methods & Examples Subject Attribute Definition Methods & Examples Status & Future Work.

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Applying dependency parses and SRL: Subject and Generic Attribute Discovery

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  1. Applying dependency parses and SRL:Subject and Generic Attribute Discovery Stephen Wu, Mayo Clinic SHARPn Summit 2012 June 11, 2012

  2. Outline • Motivation and Role • Generic Attribute • Definition • Methods & Examples • Subject Attribute • Definition • Methods & Examples • Status & Future Work

  3. Attribute Discovery • Clinical Element Models • Exclude generic • Family history • Methods: Dependency Parser and SRL

  4. Methods summary • Types of rules • Noun phrase structure • Path to root • Path between pairs • Semantic arguments • Feature vector • Decision logic/ML

  5. Generic: Attribute Definition (a) The patient was referred to the Lupusclinic. (b) We discussed increased risk of breast cancer Definition: “refers to mentions, which are generic, i.e., not related to the instance of a disorder, sign/symptom, etc…” “… Mentioned as part of a general statement with no clear subject/experiencer.” Values: in {true, false} default=false

  6. Generic: Dependency parse rules Ex: Noun phrase structure Rule (a) The patient was referred to the Lupus clinic. • Find the headword of the NE • Modifies another noun (nmod)? was vc referred sbj adv to pmod patient clinic nmod the nmod nmod the Lupus generic=true

  7. Generic: Dependency parse rules Ex: Path to root Rule (“Discussion” context) (b) We discussed increased risk of breast cancer • Find NE headword • Path to top • “Discussion” word? discussed obj risk sbj nmod of pmod We nmod cancer increased nmod breast generic=true discuss, ask, understand, understood, tell, told, mention, talk, speak, spoke, address

  8. Subject: Attribute Definition (c) The patient’s son has schizophrenia. (d) Father died of MI in 50’s Definition: “The person the observation is on. This modifier refers to the entity experiencing the disorder.” Values: in {Patient, Family_Member, default=Patient Donor_Family_Member, Donor_Other, and Other}

  9. Subject: Semantic role labeling rules Ex: Semantic argument Rule (c) The patient’s son has schizophrenia. • Semantic argument (ARG0, ARG1) • Family term (WordNet) has PRED son ARG0 schizophrenia ARG1 patient the ‘s subject=family_member father, dad, mother, mom, bro, sis, sib, cousin, aunt, uncle, grandm, grandp, grandf, wife, spouse, husband, child, offspring, progeny, son, daughter, nephew, niece, kin, family

  10. Subject: Dependency parse rules Ex: Path to root Rule (family) (d) … father who died of MI in 50's • Find NE headword • Path to top • Family term? father nmod who nmod died tmp in pmod 50s adv of pmod MI subject=family_member

  11. Subject: Dependency parse rules Ex: Dependency paths Rule (d) Father died of MI in 50's • NE + “Family” pairs • Find dependency path • Once-removed? died tmp sbj in pmod 50s Father adv of pmod MI subject=family_member

  12. Methods summary • Types of rules • Noun phrase structure • Path to root • Path between pairs • Semantic arguments • Feature vector • Decision logic/ML

  13. Status and Future Work • cTAKES v2.5 • “Assertion” module • Default • Future work (with data) • Evaulation & Error analysis • Improved rules • Features in machine learning

  14. Task 4/6 team: Stephen Wu Cheryl Clark James Masanz Matt Coarr Ben Wellner Special thanks to: Lee Becker Guergana Savova Pei Chen This work was supported in part by the SHARPn (Strategic Health IT Advanced Research Projects) Area 4: Secondary Use of EHR Data Cooperative Agreement from the HHS Office of the National Coordinator, Washington, DC. DHHS 90TR000201. https://sites.google.com/site/stephentzeinnwu wu.stephen@mayo.edu Thank you.

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