title: New challenges for text mining: mapping between text and manually curated pathways.
abstract: BACKGROUND
  Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge.
  
  
  RESULTS
  To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, 'bio-inference,' as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of 'bio-inference' schemes observed in the pathway corpus.
  
  
  CONCLUSIONS
  We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text.
authors.affiliation: Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp
authors.forename: Kanae
authors.initials: K
authors.lastname: Oda
authors.forename: Jin-Dong
authors.initials: JD
authors.lastname: Kim
authors.forename: Tomoko
authors.initials: T
authors.lastname: Ohta
authors.forename: Daisuke
authors.initials: D
authors.lastname: Okanohara
authors.forename: Takuya
authors.initials: T
authors.lastname: Matsuzaki
authors.forename: Yuka
authors.initials: Y
authors.lastname: Tateisi
authors.forename: Jun'ichi
authors.initials: J
authors.lastname: Tsujii
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004014:Dictionaries as Topic; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D009626:Terminology as Topic; D018875:Vocabulary, Controlled