• DocumentCode
    495223
  • Title

    Automatic Preposition Errors Correction Using Inductive Learning

  • Author

    Ototake, Hokuto ; Araki, Kenji

  • Author_Institution
    Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
  • Volume
    5
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    335
  • Lastpage
    338
  • Abstract
    In this paper, we describe a system for correcting English preposition errors automatically. Non-native English writers often make these errors. Our system uses rules extracted automatically based on preposition context features, such as preceding and following nouns. Additional rules are generated recursively from the extracted rules using inductive learning. Our system achieves 82% accuracy and 32% coverage, which are competitive with other systems. Apart from the performance, it has an advantage of being more understandable while investigating why a given preposition was erroneous. This is because we use rules and they give this advantage over maximum entropy approaches.
  • Keywords
    computer aided instruction; learning by example; linguistics; automatic preposition error correction; inductive learning; language learning; maximum entropy approach; nonnative English writer; preposition context feature; rule extraction; Computer errors; Computer science; Data mining; Dictionaries; Entropy; Error analysis; Error correction; Information science; Spatial databases; Writing; corpus; grammatical error correction; preposition error;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.651
  • Filename
    5170553