Paper: Annotating and Recognising Named Entities in Clinical Notes

ACL ID P09-3003
Title Annotating and Recognising Named Entities in Clinical Notes
Venue ACL-IJCNLP: Student Research Workshop papers
Session
Year 2009
Authors

This paper presents ongoing research in clinical information extraction. This work introduces a new genre of text which are not well-written, noise prone, ungrammat- ical and with much cryptic content. A cor- pus of clinical progress notes drawn form an Intensive Care Service has been manu- ally annotated with more than 15000 clin- ical named entities in 11 entity types. This paper reports on the challenges involved in creating the annotation schema, and recog- nising and annotating clinical named enti- ties. The information extraction task has initially used two approaches: a rule based system and a machine learning system using Conditional Random Fields (CRF). Different features are investigated to as- sess the interaction of feature sets and the supervised learning approaches to estab- ...