title: An environment for relation mining over richly annotated corpora: the case of GENIA.
abstract: BACKGROUND
  The biomedical domain is witnessing a rapid growth of the amount of published scientific results, which makes it increasingly difficult to filter the core information. There is a real need for support tools that 'digest' the published results and extract the most important information.
  
  
  RESULTS
  We describe and evaluate an environment supporting the extraction of domain-specific relations, such as protein-protein interactions, from a richly-annotated corpus. We use full, deep-linguistic parsing and manually created, versatile patterns, expressing a large set of syntactic alternations, plus semantic ontology information.
  
  
  CONCLUSIONS
  The experiments show that our approach described is capable of delivering high-precision results, while maintaining sufficient levels of recall. The high level of abstraction of the rules used by the system, which are considerably more powerful and versatile than finite-state approaches, allows speedy interactive development and validation.
authors.affiliation: Institute of Computational Linguistics, IFI, University of Zurich, Switzerland. rinaldi@ifi.unizh.ch
authors.forename: Fabio
authors.initials: F
authors.lastname: Rinaldi
authors.forename: Gerold
authors.initials: G
authors.lastname: Schneider
authors.forename: Kaarel
authors.initials: K
authors.lastname: Kaljurand
authors.forename: Michael
authors.initials: M
authors.lastname: Hess
authors.forename: Martin
authors.initials: M
authors.lastname: Romacker
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012660:Semantics; D012984:Software; D009626:Terminology as Topic; D018875:Vocabulary, Controlled