title: Event extraction across multiple levels of biological organization.
abstract: BACKGROUND
  Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability.
  
  
  RESULTS
  We extend the event extraction approach to biomedical information extraction to encompass all levels of biological organization from the molecular to the whole organism. We present the ontological foundations, target types and guidelines for entity and event annotation and introduce the new multi-level event extraction (MLEE) corpus, manually annotated using a structured representation for event extraction. We further adapt and evaluate named entity and event extraction methods for the new task, demonstrating that both can be achieved with performance broadly comparable with that for established molecular entity and event extraction tasks.
  
  
  BACKGROUND
  The resources and methods introduced in this study are available from http://nactem.ac.uk/MLEE/.
  
  
  BACKGROUND
  pyysalos@cs.man.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK. pyysalos@cs.man.ac.uk
authors.forename: Sampo
authors.initials: S
authors.lastname: Pyysalo
authors.forename: Tomoko
authors.initials: T
authors.lastname: Ohta
authors.forename: Makoto
authors.initials: M
authors.lastname: Miwa
authors.forename: Han-Cheol
authors.initials: HC
authors.lastname: Cho
authors.forename: Jun'ichi
authors.initials: J
authors.lastname: Tsujii
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D057225:Data Mining / Q000379:methods*; D006801:Humans; D009369:Neoplasms