한국인 영어학습자의 영어 문장은 얼마나 원어민스러운가: 딥러닝 기반 분석
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박권식 | - |
dc.contributor.author | 유석훈 | - |
dc.contributor.author | 송상헌 | - |
dc.date.accessioned | 2021-09-01T23:26:58Z | - |
dc.date.available | 2021-09-01T23:26:58Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 1738-1908 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/69409 | - |
dc.description.abstract | Building upon the state-of-the-art deep learning techniques, the present study classifies the texts written by Korean EFL learners and English native speakers and thereby demonstrates how the two types of texts differ from each other. To this end, the current work makes use of the Yonsei English Learner Corpus (YELC) and Gacheon Learner Corpus (GLC) as the L2 data, and Corpus of Contemporary American English (COCA) as the L1 data. Utilizing the sentence classification methods, the current work implements a system to differentiate the two types of texts, the accuracy of which is about 94%. This indicates that the deep leaning-based system is capable of identifying the well-formedness and felicities of the texts written by Korean EFL learners. Nonetheless, the system-based judgments do not overlap with human judgments largely because the deep learning model exclusively focuses on sequence of words. The present study provides a further analysis to see how the two types of judgments differ with respect to grammatical errors (e.g., word order, voice, etc.) and felicity errors (e.g., semantic prosody, the position of adverbs, etc.). | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 연세대학교 언어정보연구원 | - |
dc.title | 한국인 영어학습자의 영어 문장은 얼마나 원어민스러운가: 딥러닝 기반 분석 | - |
dc.title.alternative | Using the Deep Learning Techniques for Understanding the nativelikeness of Korean EFL Learners | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 송상헌 | - |
dc.identifier.doi | 10.20988/lfp.2019.48..195 | - |
dc.identifier.bibliographicCitation | 언어사실과 관점, v.48, pp.195 - 227 | - |
dc.relation.isPartOf | 언어사실과 관점 | - |
dc.citation.title | 언어사실과 관점 | - |
dc.citation.volume | 48 | - |
dc.citation.startPage | 195 | - |
dc.citation.endPage | 227 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002527616 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | 딥러닝 | - |
dc.subject.keywordAuthor | 분류 | - |
dc.subject.keywordAuthor | 학습자 코퍼스 | - |
dc.subject.keywordAuthor | 오류 분석 | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | learner corpus | - |
dc.subject.keywordAuthor | error analysis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.