• DocumentCode
    464193
  • Title

    Learning Relations and Information Extraction Rules for Protein Annotation

  • Author

    Kim, Jee-Hyub ; Artificial, M.H.

  • Author_Institution
    Artificial Intell. Lab., Univ. of Geneva, Geneva
  • Volume
    1
  • fYear
    2007
  • fDate
    21-23 May 2007
  • Firstpage
    349
  • Lastpage
    354
  • Abstract
    Protein annotation is a task that describes protein X in terms of topic Y Until now, most of protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever rapidly, manual annotation becomes difficult, and there is increasing need to automate the protein annotation process. Recently, Information Extraction (IE) has been used to solve this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real world domains such as the biomedical domain. In this paper, we describe an IE system which requires only sentences labeled relevant or not to a given topic by domain experts.
  • Keywords
    biology computing; knowledge acquisition; proteins; human annotators; information extraction rules; manual annotation; protein annotation process; Artificial intelligence; Biomedical engineering; Data mining; Databases; Humans; Knowledge engineering; Laboratories; Learning; Protein engineering; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on
  • Conference_Location
    Niagara Falls, Ont.
  • Print_ISBN
    978-0-7695-2847-2
  • Type

    conf

  • DOI
    10.1109/AINAW.2007.220
  • Filename
    4221084