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Improving the Performance of Statistical Context-Sensitive Spelling Error Correction Techniques Using Default Operation Algorithm
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Default 연산 알고리즘을 적용한 통계적 문맥의존 철자오류 교정 기법의 성능 향상

논문 기본 정보

Type
Proceeding
Author
Lee, Jung-Hun (부산대학교) Kim, Minho (부산대학교) Kwon, Hyuk-Chul (부산대학교)
Journal
한국어정보학회 한국어정보학회 학술대회 한국어정보학회 2016년도 제28회 한글및한국어정보처리학술대회
Published
2016.1
Pages
165 - 170 (6page)

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Improving the Performance of Statistical Context-Sensitive Spelling Error Correction Techniques Using Default Operation Algorithm
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Research history (8)

2022

Performance Improvement of Context-Sensitive Spelling Error Correction Techniques using Knowledge Graph Embedding of Korean WordNet (alias. KorLex)
Jung-Hun Lee ,  Sanghyun Cho ,  Hyuk-Chul Kwon Journal of Korea Multimedia Society 2022.03 Academic journal

2021

Context-sensitive Spelling Error Correction Based on Unsupervised Learning Neural Language Model
이정훈 정보융합공학과 2021.01 Thesis

2017

The Utilization of Local Document Information to Improve Statistical Context-Sensitive Spelling Error Correction
Jung-Hun Lee ,  Minho Kim ,  Hyuk-Chul Kwon KIISE Transactions on Computing Practices 2017.07 Academic journal
Improving the Performance of Statistical Context-Sensitive Spelling Error Correction Techniques Using Default Operation Algorithm and interpolation
이정훈 전기전자컴퓨터공 2017.01 Thesis
Improved Statistical Language Model for Context-sensitive Spelling Error Candidates
Jung-Hun Lee ,  Minho Kim ,  Hyuk-Chul Kwon Journal of Korea Multimedia Society 2017.01 Academic journal

2016

Local Document Frequency using Statical Context-Sensitive Spelling Error Correction Techniques
Jung-Hun Lee ,  Minho Kim ,  Hyuk-Chul Kwon 한국정보과학회 학술발표논문집 2016.12 Proceeding
Improving the Performance of Statistical Context-Sensitive Spelling Error Correction Techniques Using Interpolation Smoothing between Corpora
Jung-Hun Lee ,  Minho Kim ,  Hyuk-Chul Kwon 한국정보과학회 학술발표논문집 2016.06 Proceeding
Improving the Performance of Statistical Context-Sensitive Spelling Error Correction Techniques Using Default Operation Algorithm
Lee ,  Jung-Hun ,  Kim 한국어정보학회 학술대회 2016.01 Proceeding
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Abstract· Keywords

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본 논문에서 제안하는 문맥의존 철자오류 교정은 통계 정보를 이용한 방법으로 통계적 언어처리에서 가장 널리 쓰이는 샤논(Shannon)이 발표한 노이지 채널 모형(noisy channel model)을 기반으로 한다. 선행연구에서 부족하였던 부분의 성능 향상을 위해 교정대상단어의 오류생성 및 통계 데이터의 저장 방식을 개선하여 Default 연산을 적용한 모델을 제안한다. 선행 연구의 모델은 교정대상단어의 오류생성 시 편집거리의 제약을 1로 하여 교정 실험을 하지만 제안한 모델은 같은 환경에서 더욱 높은 검출과 정확도를 보였으며, 오류단어의 편집거리(edit distance) 제약을 넓게 적용하더라도 신뢰도가 있는 검출과 교정을 보였다.

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