• Annual Conference on Human and Language Technology
• /
• 2021.10a
• /
• pp.293-299
• /
• 2021

본 논문은 외국어 학습을 위한 딥러닝 기반 영어 교육 플랫폼인 PEEP-Talk (Personalized English Education Platform)을 제안한다. PEEP-Talk는 딥러닝 기반 페르소나 대화 시스템과 영어 문법 교정 피드백 기능이 내장된 교육용 플랫폼이다. 또한 기존 페르소나 대화시스템과 다르게 대화의 흐름이 벗어날 시 이를 자동으로 판단하여 대화 주제를 실시간으로 변경할 수 있는 CD (Context Detector) 모듈을 제안하며 이를 적용하여 실제 사람과 대화하는 듯한 느낌을 사용자에게 줄 수 있다. 본 논문은 PEEP-Talk의 각 모듈에 대한 정량적인 분석과 더불어 CD 모듈을 객관적으로 판단할 수 있는 새로운 성능 평가지표인 CDM (Context Detector Metric)을 기반으로 PEEP-Talk의 강건함을 검증하였다. 이와 더불어 PEEP-Talk를 카카오톡 채널을 이용하여 배포하였다.

• Annual Conference on Human and Language Technology
• /
• 2012.10a
• /
• pp.195-200
• /
• 2012

한국어 조사는 다른 외국어에는 대응하는 어휘가 없는 경우가 대부분이기 때문에 외국인이 한국어를 배울때 조사를 가장 어려워한다. 특히, 한국어에서 조사는 문법적 특징을 결정하는 매우 중요한 형태소이며 문장의 뜻을 매우 다르게 바꿀 수 있으므로 올바른 사용이 필수적이다. 본 논문에서는 외국민이 입력한 불완전한 한국어 문장에서 조사를 올바르게 교정하는 방법을 제안한다. 이 방법은 주어진 문장에 대해 한국어 형태소 분석기와 품사 태거를 이용하여 체언과 용언을 추출하고 이를 세종 용언 사전과 체언 사전의 문형 정보를 이용하여 올바른 조사를 부착하고 교정해 준다.

• The KIPS Transactions:PartB
• /
• v.18B no.6
• /
• pp.405-414
• /
• 2011

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules that are manually formulated by a language expert. These rules are appended each time a new error pattern is detected. However, such grammar checkers are not consistent. In order to resolve this shortcoming, we propose new method for generalizing error decision rules to detect the above errors. For this purpose, we use an existing thesaurus KorLex, which is the Korean version of Princeton WordNet. KorLex has hierarchical word senses for nouns, but does not contain any information about the relationships between cases in a sentence. Through the Tree Cut Model and the MDL(minimum description length) model based on information theory, we extract noun classes from KorLex and generalize error decision rules from these noun classes. In order to verify the accuracy of the new method in an experiment, we extracted nouns used as an object of the four predicates usually confused from a large corpus, and subsequently extracted noun classes from these nouns. We found that the number of error decision rules generalized from these noun classes has decreased to about 64.8%. In conclusion, the precision of our grammar checker exceeds that of conventional ones by 6.2%.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.6
• /
• pp.9-15
• /
• 2021

The current study examined the cyber learners' perceptions of the educational usefulness of Grammarly, an online grammar checker, after it was used to provide feedback to cyber university students in a situation where the instructor could not provide sufficient feedback on their written work in English. The survey results, revealed that the majority of learners had positive attitudes to the usefulness of Grammarly. In particular, the feedback immediately available whenever needed was regarded as helpful in improving English sentences, and most learners were highly satisfied with the amount of the feedback provided by Grammarly. It was also found that Grammarly had positive effects in terms of the affective domains, helping learners to improve their interest and confidence in English writing. In particular, Grammarly was found to be effective in reducing writing anxiety in English, one of the main factors negatively affecting writing performance in English. However, along with these positive results, limitations such as inaccurate feedback and inadequate explanation of errors were also found. Therefore, when Grammarly is used for English education, it is necessary to conduct multifaceted research to develop effective teaching methods that can minimize the problems that may arise from these limitations.

• Journal of The Korean Association of Information Education
• /
• v.21 no.2
• /
• pp.161-169
• /
• 2017

We often use spellcheckers in order to correct the syntactic errors in our documents. However, these computer programs are not enough for elementary school students, because their sentences are not smooth even after correcting the syntactic errors in many cases. In this paper, we introduce an automated method for evaluating the smoothness of two synonymous sentences. This method uses a recurrent neural network to solve the problem of long-term dependencies and exploits subwords to cope with the rare word problem. We trained the recurrent neural network language model based on a monolingual corpus of about two million English sentences. In our experiments, the trained model successfully selected the more smooth sentences for all of nine types of test set. We expect that our approach will help in elementary school writing after being implemented as an application for smart devices.