title: Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis.
abstract: BACKGROUND
  Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems.
  
  
  RESULTS
  In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge.
  
  
  CONCLUSIONS
  For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates.
authors.forename: Kai
authors.initials: K
authors.lastname: Hakala
authors.forename: Sofie
authors.initials: S
authors.lastname: Van Landeghem
authors.forename: Tapio
authors.initials: T
authors.lastname: Salakoski
authors.forename: Yves
authors.initials: Y
authors.lastname: Van de Peer
authors.forename: Filip
authors.initials: F
authors.lastname: Ginter
mesh_terms: D057225:Data Mining; D053263:Gene Regulatory Networks; D058977:Molecular Sequence Annotation; D009323:Natural Language Processing