• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.472-478
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• 2001

본 논문은 문맥의 공기정보를 사용한 한국어 명사의 의미구분에 관한 연구이다. 대상 명사에 대한 문맥의 지엽적인 단어분포는 명사의 의미구분을 위한 의미적 특성을 표현하는데 충분하지 못하다. 본 논문은 의미별로 수집한 문맥 정보를 기저 벡터화 하는 방법을 제안한다. 정보의 중요도 측정을 통하여 의미구분에 불필요한 문맥정보는 제거하고, 남아있는 문맥의 단어들은 변별력 강화를 위하여 상의어 정보로 바꾸어 기저벡터에 사용한다. 상의어 정보는 단어의 형태와 사전 정의문의 패턴을 통해 추출한다. 의미 벡터를 통한 의미구분에 실패하였을 경우엔 훈련데이터에서 가장 많이 나타난 의미로 정답을 제시한다. 실험을 위해 본 논문에서는 SENSEVAL 실험집합을 사용하였으며, 제시한 방법으로 공기정보의 가공 없이 그대로 실험한 방법과 비교하여 최고 42% 정도의 정확률 향상을 나타내었다.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.113-117
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• 2008

본 논문에서는 패턴기반 자동번역시스템의 효율적인 번역 성능 향상을 위해 병렬 코퍼스(parallel corpus)를 이용한 오류 자동 탐지 방법을 제안하고자 한다. 번역시스템에 존재하는 대부분 오류는 크게 지식 오류와 엔진 오류로 나눌 수 있는데 통상 이런 오류는 이중 언어가 가능한 훈련된 언어학자가 대량의 자동번역 된 결과 문장을 읽음으로써 오류를 탐지하고 분석하여 번역 지식을 수정/확장하거나 또는 엔진을 개선하게 된다. 하지만, 이런 작업은 많은 시간과 노력을 필요로 하게 된다. 따라서 본 논문에서는 병렬 코퍼스 중의 목적 언어(Target Language) 문장 즉, 정답 문장과 자동번역 된 결과 문장을 다양한 방법으로 비교하면서 번역시스템에 존재하고 있는 지식 및 엔진 오류를 자동으로 탐지하는 방법을 제안한다. 제안한 방법은 한-중 자동번역시스템에 적용하여 그 정확률과 재현률을 측정하였으며, 자동적으로 오류를 탐지하여 추출 할 수 있음을 증명하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.816-818
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• 2015

본 논문에서는 질의유형을 통한 검색기반 질의응답 시스템을 구현하기 위한 설계방법을 제안한다. 이를 위해 위키피디아 문서의 링크 데이터를 이용하여 색인 대상문서와 데이터베이스를 구축하는 색인 모델과 2-포아송 모델을 이용하여 얻은 문서들을 색인 데이터베이스를 통해 필터링하여 정답 후보문장을 추출하는 검색모델, 키워드 패턴 매칭 기반 질의유형 분류 모델을 설계하였다.

• Journal of the Korean Chemical Society
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• v.54 no.6
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• pp.818-828
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• 2010

It has already been 17 years since the first implementation of the Korean College Scholastic Ability Test (CSAT). Having been administered so many CSAT tests including practice tests, criticisms have been made against CAST tests being stuck to the same pattern and focusing mainly on knowledge-based items. To address this issue, we analyzed the chemistry items of the Japanese National Center Test for University Admissions (NCTUA) administered in January of 2009 with regard to content factors, behavioral domains, item types, and noted any peculiarities in comparison to CSAT. Also, we estimated the predicted percentage of correct answers from the perspectives of Korean candidates to arrive at implications for chemistry items of CSAT.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.218-223
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• 2009

This dissertation about the wrong answer note model development which is individualized investigates a problem. The method which is used from here uses a group sincerity and adds wrong answer analysis leads and to the wrong answer person explanation note of the pattern which is similar refers a wrong answer note explanation. The result which this dissertation is principal will reach to the wrong answer where is not the explanation about right answer and the process which is incorrect and a wrong answer will seek will be able to arrange. There is a possibility of finding the solution which existing wrong answer note system is improved with the method which is proposed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.191-201
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• 2014

The purpose of this study is to propose strategies of puzzle-based learning for Informatics gifted education through analyzing Informatics gifted elementary students' computational problem solving approaches in puzzle-based learning contexts. Six types of educational puzzles, which are constraints, optimization, probability, statistically speaking, pattern recognition, and strategy, were used in teaching 14 Informatics gifted students for 8 sessions. The results of pre and post test and each students' answers were analyzed to identify why students were not able to solve the puzzles. We also analysed what essential computational strategies are needed to solve each type of puzzles, and what students did not know in solving puzzle problems. We identified some problems caused by puzzle representation methods, and various students' intuitions that disturb puzzle solving. Also, we identified essential computational strategies to solve puzzles: backtracking, dynamic programming, abstraction, modeling, and reduction of big problem. However, students had difficulties in applying these strategies to solve their puzzle problems. We proposed the revised puzzle-based learning strategies, which is based on the improved problem representation, just-in-time cognitive feedbacks, and web-based learning system.

• Journal of the Korean School Mathematics Society
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• v.19 no.3
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• pp.261-275
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• 2016

Many students have dualistic beliefs about mathematics and its learning- for example, there is always just one right answer in mathematics and their role in the classroom is receiving and absorbing knowledge from teacher and textbook. This article investigated some epistemic implications and limitations of common mathematics teaching practices, which often present mathematical facts(or procedures) and treat students' errors in a certain and absolute way. Langer and Piper's (1987) experiment and Oliveira et al.'s (2012) study suggested that presenting knowledge in conditional language which allows uncertainty can foster students' productive epistemological beliefs. Changing the focus and patterns of classroom communication about students' errors could help students to overcome their dualistic beliefs. This discussion will contribute to analyze the implicit epistemic messages conveyed by mathematics instructions and to investigate teaching strategies for stimulating students' epistemic development in mathematics.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.29-36
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• 2006

Tactile displays can provide useful information without disturbing others and are particularly useful for people with visual or auditory impairments. They can also complement other displays. In this paper, we present a new vibrotactile display device for wearable, mobile, and ubiquitous computing environments. The proposed vibrotactile device has a $5{\times}5$ array configuration for displaying complex information such as letters, numbers, and haptic patterns as well as simple directional ques and situation awareness alarms. Commercially available coin-type vibration motors are embedded vertically in flexible mounting pads in order to best localize vibrations on the skin. An embedded microprocessor controls the motors sequentially with an advanced tracing mode to increase recognition rate. User studies with the vibrotactile device on the top of the foot show 86.7% recognition rate for alphabet characters after some training. In addition, applying vibrotactile device to driving situation shows 83.9% recognition rate. We also propose some potentially useful application scenarios including Caller Identification for mobile phones and Navigation Aids for GPS systems while driving.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.61-68
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• 2008

This paper presents a spam filter system using co-training with spamminess features and URL features based on the maximum entropy model. Spamminess features are the emphasizing patterns or abnormal patterns in spam messages used by spammers to express their intention and to avoid being filtered by the spam filter system. Since spammers use URLs to give the details and make a change to the URL format not to be filtered by the black list, normal and abnormal URLs can be key features to detect the spam messages. Co-training with spamminess features and URL features uses two different features which are independent each other in training. The filter system can learn information from them independently. Experiment results on TREC spam test collection shows that the proposed approach achieves 9.1% improvement and 6.9% improvement in accuracy compared to the base system and bogo filter system, respectively. The result analysis shows that the proposed spamminess features and URL features are helpful. And an experiment result of the co-training shows that two feature sets are useful since the number of training documents are reduced while the accuracy is closed to the batch learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.