한국인 영어학습자의 영어 문장은 얼마나 원어민스러운가: 딥러닝 기반 분석
Using the Deep Learning Techniques for Understanding the nativelikeness of Korean EFL Learners
0
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
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.).
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.).
참고문헌 (23)
- 고광윤, "한국의 교과서 영어는 얼마나 영어다운가", 영어학, 7(2), pp.191~210 (2007), 한국영어학회[DOI]
- 고광윤, "교과서 영어의 영어다움에 대한 코퍼스 기반 분석: 기능어의 쓰임을 중심으로", 영어학연구, (26), pp.1~19 (2008), 한국영어학학회[DOI]
- 김지섭, "딥러닝 기반 비디오 분석 기술", 정보과학회지, 33(9), pp.21~31 (2015), 한국정보과학회
- 박선옥, "한국인 영어교사와 학생들의 작문에 나타난 관사사용의 분포와 오류유형 분석", 언어과학연구, (38), pp.161~185 (2006), 언어과학회
- 전지현, "한국대학생 영작문 코퍼스에 나타난 오류분석", 언어과학, 20(4), pp.135~155 (2013), 한국언어과학회
- 천승미, "성인영어학습자의 발화오류분석을 통한 전치사 사용 연구", 외국어교육연구, 32(3), pp.229~256 (2018), 외국어교육연구소[DOI]
- 최연정, "한국 대학 신입생들의 영작문 코퍼스에 나타난 V-ing 오류분석", 영어교육, 67(4), pp.311~335 (2012), 한국영어교육학회[DOI]
- Alext Warstadt, "Neural Network Acceptability Judgements", arXiv preprint arXiv:1805.12471, ~ (2018)
- Francis Bond, "Improving Statistical Machine Translation by Paraphrasing the Training Data", Proceedings of IWSLT 2008, pp.150~157 (2008)
- Francis Bond, "Enhancing the Japanese WordNet", Proceedings of the 7th Workshop on Asian Language Resources, ACL-IJCNLP 2009, pp.1~8 (2009)
- Kachru, Braj B, "English in the World: Teaching and Learning the Language and Literatures", pp.11~30 (1985), Cambridge University Press
- Kowsari, "Text Classification Algorithms: A Survey", Information, 10, pp.150~ (2019)
- Louw, B, "Words in Context: A Tribute to John Sinclair on his Retirement" (2000), University of Birmingham
- Partington, A., "‘‘Utterly content in each other’s company’’: Semantic prosody and semantic preference", International Journal of Corpus Linguistics, 9(1), pp.131~156 (2004)
- Pawley, Andrew, "Language and Communication", pp.191~225 (1983), Longman
- Richard Xiao, "Collocation, Semantic Prosody, and Near Synonymy: A Cross-Linguistic Perspective", Applied Linguistics, 27(1), pp.103~129 (2006), Oxford University Press
- Shirley N. Dita, "Stance Adverbs in Asian Englishes: A corpus-based study", 17th IAWE Conference (2011), Monash University
- 송상헌, "Phraseological Analysis of Learner Corpus Based on Language Model", 언어와 정보, 22(1), pp.123~152 (2018), 한국언어정보학회[DOI]
- Stubbs, M., "Collocations and semantic profiles: On the cause of trouble with quantitative studies", Functions of Language, 2(1), pp.23~55 (1995)
- Thushan Ganegedara, "Natural Language Processing with TensorFlow" (2018), Packt Publishing
- Tony McEnery, "Corpus Linguistics: Method, Theory and Practice", pp.233~236 (2012), Cambridge University Press
- Yann LeCun, "Deep learning", Nature, 521, pp.436~444 (2015)
- Zethsen, K. K., "Semantic prosody: Creating awareness about a versatile tool", Tidsskrift for Sprogforskning, 4(1), pp.275~294 (2006)