• DocumentCode
    511288
  • Title

    First-Level Bottom-Up Parser

  • Author

    Zhou, Xunwei

  • Author_Institution
    Inst. of Inf. Technol., Beijing Union Univ. Beijing, Beijing, China
  • Volume
    1
  • fYear
    2009
  • fDate
    25-27 Dec. 2009
  • Firstpage
    192
  • Lastpage
    195
  • Abstract
    When a human being makes hypothetical inference, he makes recessive hypothetical inference. When a computer makes hypothetical inference, it makes dominant hypothetical inference. Rule-based parsers are divided into inference rule parsers and hypothetical inference parsers. Inference rule parsers are based on recessive hypothetical inference, they are suitable for human. Hypothetical inference parsers are based on dominant hypothetical inference, they are suitable for computer. A manual parser is an inference rule parser. First-level bottom-up parser proposed in this paper is a hypothetical inference parser. A manual parser can be transformed into first-level bottom-up parser by changing the grammar rules from inference rules to axioms, taking hypothetical inference as the sole inference rule. Compared with DCG, another hypothetical inference parser, first-level bottom-up parser is simpler and more powerful in semantic analysis.
  • Keywords
    grammars; inference mechanisms; knowledge based systems; bottom-up parser; dominant hypothetical inference; grammar rules; hypothetical inference parsers; inference rule parsers; recessive hypothetical inference; rule-based parsers; semantic analysis; Application software; Computer applications; Humans; Information technology; Natural languages; Writing; dominant hypothetical inference; first-level bottom-up parser; hypothetical inference parser; inference rule parser; recessive hypothetical inference;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-0-7695-3930-0
  • Electronic_ISBN
    978-1-4244-5423-5
  • Type

    conf

  • DOI
    10.1109/IFCSTA.2009.54
  • Filename
    5385101