• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.954-957
• /
• 2019

한국어 맞춤법 검사기는 현재 여러 곳에서 개발하여 서비스를 제공하고 있으며 다양한 목적으로 사용되고 있다. 하지만 이들은 맞춤법이나 띄어쓰기를 검사할 뿐, 존댓말과 같은 높임표현이 잘못 사용되었는지는 검사할 수 없다. 최근에는 맞춤법 검사를 위해 통계적 방식과 딥러닝을 사용하고 있지만, 본 연구에서는 규칙 기반을 활용하여 사용자가 텍스트를 입력하면 잘못된 높임표현을 탐지하여 그에 대한 오류 정보를 제공하고, 올바른 표현으로 교정하는 프로그램을 개발하였다. 구문 분석기를 사용하여 주어-서술어 구조를 파악하고, 형태소 분석기를 활용하여 높임표현을 탐지한 후 격틀사전의 정보를 이용하여 존댓말 오류를 탐지한다. 본 연구는 격식이 있는 문서를 검사할 때나, 한국어 높임말에 익숙하지 않은 외국인들에게 도움이 될 수 있을 것이다.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.3
• /
• pp.274-284
• /
• 2002

Word segmentation errors occurring in text preprocessing often insert incorrect words into recognition vocabulary and cause poor language models for Korean large vocabulary continuous speech recognition. We propose an automatic word segmentation algorithm using Markov chains and syllable-based n-gram language models in order to correct word segmentation error in teat corpora. We assume that a sentence is generated from a Markov chain. Spaces and non-space characters are generated on self-transitions and other transitions of the Markov chain, respectively Then word segmentation of the sentence is obtained by finding the maximum likelihood path using syllable n-gram scores. In experimental results, the algorithm showed 91.58% word accuracy and 96.69% syllable accuracy for word segmentation of 254 sentence newspaper columns without any spaces. The algorithm improved the word accuracy from 91.00% to 96.27% for word segmentation correction at line breaks and yielded the decomposition accuracy of 96.22% for compound-noun decomposition.

• The Journal of Korean Association of Computer Education
• /
• v.7 no.1
• /
• pp.141-149
• /
• 2004

Today there are many Web courseware systems for formative evaluation and feedback. Formative evaluation and feedback provided according to users' response in most Web courseware systems, however, are simple texts showing only whether correct or wrong, correct answers, relevant information, etc., far deviated from actual context. Thus such a system may weaken the corrective function of feedback and, as a result, reduce learners' understanding of contents and the possibility of learning transfer. In addition, according to the learning theory of constructivism, learning is influenced by the situation, in which it happens, and knowledge is learned and transferred differently depending on the context in which it is learned. In the background, this study designed and implemented a contextual error-correction feedback system that can provide feedback in a context closely related and similar to the relevant situation according to the response of learners when formative evaluation is carried out in Web courseware. In addition, it applied 'correction/correct-answer-providing feedback', 'relevant information providing feedback' and 'contextual error-correction feedback' to Web courseware for learning actual productivity S/W and verified if 'contextual error-correction feedback' is more effective than other two types of feedback for learners' academic achievement.

• Journal of The Korean Association of Information Education
• /
• v.3 no.1
• /
• pp.94-115
• /
• 1999

Multimedia CAI programs have been produced which encourage self-study and positive learning activities. With instant feedback from the CAI program, students' errors can be corrected easily and the composition ability can be advanced have been done. To increase students' computer capability and composition abilities the following studies. 1. Make an estimate about the factors affecting composition themes and prepare a course based on individual students' needs. 2. Design and implement CAI programs for the enhancement of composition ability. 3. Find an effective teaching-learning model for the generalization of the CAI title and then verify the results.