1 / 14

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.

kisha
Télécharger la présentation

Applying dependency parses and SRL: Subject and Generic Attribute Discovery

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related