The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
제안된 영어 작문 시험 자동 채점 시스템은 사람의 노력 없이도 응시자에게 점수와 진단 피드백을 포함한 평가 결과를 제공한다. 시스템은 입력 문장을 분석하고 철자, 구문 및 내용 유사성과 관련된 오류를 감지합니다. 채점 모델은 통계적 접근 방식 중 하나인 회귀 트리를 채택했습니다. 일반적으로 채점 모델은 자동으로 감지된 오류의 개수와 유형을 기반으로 점수를 계산합니다. 따라서 오류 감지 정확도가 높은 시스템일수록 테스트 채점 정확도도 높아집니다. 그러나 구문 분석 실패, 지식 기반의 불완전성, 자연어의 모호한 특성 등 여러 가지 이유로 시스템의 정확성을 완전히 보장할 수는 없습니다. 본 논문에서는 정보 검색에 널리 사용되는 용어 가중치와 유사한 오류 가중치 기법을 소개합니다. 오류가중법은 시스템에서 감지된 오류의 신뢰성을 판단하기 위해 적용됩니다. 이 기법으로 계산된 점수는 기법을 사용하지 않은 점수보다 더 정확한 것으로 입증되었습니다.
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부
Kong-Joo LEE, Jee-Eun KIM, "Improving Automatic English Writing Assessment Using Regression Trees and Error-Weighting" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 8, pp. 2281-2290, August 2010, doi: 10.1587/transinf.E93.D.2281.
Abstract: The proposed automated scoring system for English writing tests provides an assessment result including a score and diagnostic feedback to test-takers without human's efforts. The system analyzes an input sentence and detects errors related to spelling, syntax and content similarity. The scoring model has adopted one of the statistical approaches, a regression tree. A scoring model in general calculates a score based on the count and the types of automatically detected errors. Accordingly, a system with higher accuracy in detecting errors raises the accuracy in scoring a test. The accuracy of the system, however, cannot be fully guaranteed for several reasons, such as parsing failure, incompleteness of knowledge bases, and ambiguous nature of natural language. In this paper, we introduce an error-weighting technique, which is similar to term-weighting widely used in information retrieval. The error-weighting technique is applied to judge reliability of the errors detected by the system. The score calculated with the technique is proven to be more accurate than the score without it.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2281/_p
부
@ARTICLE{e93-d_8_2281,
author={Kong-Joo LEE, Jee-Eun KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Automatic English Writing Assessment Using Regression Trees and Error-Weighting},
year={2010},
volume={E93-D},
number={8},
pages={2281-2290},
abstract={The proposed automated scoring system for English writing tests provides an assessment result including a score and diagnostic feedback to test-takers without human's efforts. The system analyzes an input sentence and detects errors related to spelling, syntax and content similarity. The scoring model has adopted one of the statistical approaches, a regression tree. A scoring model in general calculates a score based on the count and the types of automatically detected errors. Accordingly, a system with higher accuracy in detecting errors raises the accuracy in scoring a test. The accuracy of the system, however, cannot be fully guaranteed for several reasons, such as parsing failure, incompleteness of knowledge bases, and ambiguous nature of natural language. In this paper, we introduce an error-weighting technique, which is similar to term-weighting widely used in information retrieval. The error-weighting technique is applied to judge reliability of the errors detected by the system. The score calculated with the technique is proven to be more accurate than the score without it.},
keywords={},
doi={10.1587/transinf.E93.D.2281},
ISSN={1745-1361},
month={August},}
부
TY - JOUR
TI - Improving Automatic English Writing Assessment Using Regression Trees and Error-Weighting
T2 - IEICE TRANSACTIONS on Information
SP - 2281
EP - 2290
AU - Kong-Joo LEE
AU - Jee-Eun KIM
PY - 2010
DO - 10.1587/transinf.E93.D.2281
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2010
AB - The proposed automated scoring system for English writing tests provides an assessment result including a score and diagnostic feedback to test-takers without human's efforts. The system analyzes an input sentence and detects errors related to spelling, syntax and content similarity. The scoring model has adopted one of the statistical approaches, a regression tree. A scoring model in general calculates a score based on the count and the types of automatically detected errors. Accordingly, a system with higher accuracy in detecting errors raises the accuracy in scoring a test. The accuracy of the system, however, cannot be fully guaranteed for several reasons, such as parsing failure, incompleteness of knowledge bases, and ambiguous nature of natural language. In this paper, we introduce an error-weighting technique, which is similar to term-weighting widely used in information retrieval. The error-weighting technique is applied to judge reliability of the errors detected by the system. The score calculated with the technique is proven to be more accurate than the score without it.
ER -