A Computational Korean Lexical Access Model Using Artificial Neural Network

  • Heui Seok Lim
  • Kichun Nam
  • Kinam Park
  • Sungho Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


In this paper, we propose a computational Korean lexical access model based on connectionist approach. The model is designed to simulate the behaviors observed in human lexical decision task. The proposed model adopts a simple recurrent neural network architecture which takes a Korean string of 2-syllable length as an input and makes an output as a semantic vector representing semantic of the input. As experimental results, the model shows similar behaviors of human lexical decision task such as frequency effect, lexical status effect, word similarity effect, semantic priming effect, and visual degradation effect.


Lexical Decision Lexical Decision Task Lexical Access Letter String Hide Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Heui Seok Lim
    • 1
  • Kichun Nam
    • 2
  • Kinam Park
    • 3
  • Sungho Cho
    • 4
  1. 1.Division of Computer, Information, and SoftwareHanshin UniversityKorea
  2. 2.Department of PsychologyKorea UniversityKorea
  3. 3.Department of Computer EducationKorea UniversityKorea
  4. 4.Department of Information CommunicationHanshin UniversityKorea

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