Applying dependency parses and SRL:

Subject and Generic Attribute Discovery

Stephen Wu, Mayo Clinic

SHARPn Summit 2012June 11, 2012

Outline

• Motivation and Role

• Generic Attribute• Definition• Methods & Examples

• Subject Attribute• Definition• Methods & Examples

• Status & Future Work

Attribute Discovery

• Clinical Element Models

• Exclude generic

• Family history

• Methods: Dependency Parser and SRL

Methods summary

• Types of rules• Noun phrase structure• Path to root• Path between pairs• Semantic arguments

• Feature vector

• Decision logic/ML

(a) The patient was referred to the Lupus clinic.

(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

Generic: Attribute Definition

Ex: Noun phrase structure Rule (a) The patient was referred to the Lupus clinic.

• Find the headword of the NE

• Modifies another noun (nmod)?

Generic: Dependency parse rules

referred

patient

wassbj adv

nmod

to

the

pmod

vc

nmod clinic

the

nmod

Lupus

generic=true

Ex: Path to root Rule (“Discussion” context) (b) We discussed increased risk of breast cancer

• Find NE headword

• Path to top

• “Discussion” word?

Generic: Dependency parse rules

increased

discussedsbj nmod

nmod

risk

We

pmod

obj

of

breast

nmod cancer

discuss, ask, understand, understood, tell, told,

mention, talk, speak, spoke, address

generic=true

(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=PatientDonor_Family_Member, Donor_Other, and Other}

Subject: Attribute Definition

Ex: Semantic argument Rule (c) The patient’s son has schizophrenia.

• Semantic argument (ARG0, ARG1)

• Family term (WordNet)

Subject: Semantic role labeling rules

‘s

patient

hasPRED

the

schizophreniaARG1

subject=family_member

sonARG0

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

Ex: Path to root Rule (family) (d) … father who died of MI in 50's

• Find NE headword

• Path to top

• Family term?

Subject: Dependency parse rules

MI

father

pmod

diedpmod

advin

50s

tmp

subject=family_member

of

who

nmodnmod

Ex: Dependency paths Rule (d) Father died of MI in 50's

• NE + “Family” pairs

• Find dependency path

• Once-removed?

Subject: Dependency parse rules

MI

Father pmod

diedpmod

advin

50s

tmp

subject=family_member

of

sbj

Methods summary

• Types of rules• Noun phrase structure• Path to root• Path between pairs• Semantic arguments

• Feature vector

• Decision logic/ML

Status and Future Work

• cTAKES v2.5• “Assertion” module• Default

• Future work (with data)• Evaulation & Error analysis• Improved rules• Features in machine learning

THANK YOU.

https://sites.google.com/site/stephentzeinnwu

wu.stephen@mayo.edu

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.