title: Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text.
abstract: BACKGROUND
  Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.
  
  
  RESULTS
  Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships.
  
  
  METHODS
  The source code, evaluations, documentation and other supplementary materials are available at http://www.chibi.ubc.ca/WhiteText.
  
  
  BACKGROUND
  paul@chibi.ubc.ca.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics Online.
authors.affiliation: Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
authors.forename: Leon
authors.initials: L
authors.lastname: French
authors.forename: Suzanne
authors.initials: S
authors.lastname: Lane
authors.forename: Lydia
authors.initials: L
authors.lastname: Xu
authors.forename: Celia
authors.initials: C
authors.lastname: Siu
authors.forename: Cathy
authors.initials: C
authors.lastname: Kwok
authors.forename: Yiqi
authors.initials: Y
authors.lastname: Chen
authors.forename: Claudia
authors.initials: C
authors.lastname: Krebs
authors.forename: Paul
authors.initials: P
authors.lastname: Pavlidis
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D009445:Neuroanatomy; D010506:Periodicals as Topic; D051381:Rats; D012984:Software