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Context-Sensitive Spelling Error Correction Techniques in Korean Documents using Generative Adversarial Network

생성적 적대 신경망(GAN)을 이용한 한국어 문서에서의 문맥의존 철자오류 교정

  • Lee, Jung-Hun (Grand Information Technology Research Center) ;
  • Kwon, Hyuk-Chul (Dept. of Information Computer Science., College of Eng., Pusan National University)
  • 이정훈 ;
  • 권혁철
  • Received : 2021.07.09
  • Accepted : 2021.10.13
  • Published : 2021.10.30

Abstract

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

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