• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.79-94
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• 2015

The purpose of this study is to analyze the emotion and sensibility of fashion words in terms of words types, such as loanword and Korean words, Hangul in fashion product label and fashion manufacturing industry. We surveyed 200 persons in their 20s using the questionnaire on the stimulus of product tag label and fashion words with 15 adjectives. Based on daily usage of foreign words in fashion market, we selected 1 item label in 3 forms: 1) Hangul label written in loan words 2) Label written in English and 3) Label written in Hangul and 3 fashion words in 2 forms 1) loanword and 2) Hangul. And the label types and fashion words were analyzed and investigated in terms of consumer's sensibility, preferences and estimated product price. The results are following: consumers preferred loanword label than Hangul label, and they preferred loanword in English than that in Korean. They evaluated loanword more positively, such as refinement, gorgeous and elegant, etc. and estimated the product price of loanword label as higher. But in the sensibility of 'familiar' and 'stability', Hangul label was not significantly different to loanword written in Hangul. That is, label written in English is the highest in all the evaluation, and loanword label written in Hangul is next, and Hangul label showed the lowest result. Consumers showed the evaluation differently between loanwords and Hangul according to their degree in fashion involvement. Consumers of high fashion involvement evaluated the sensibilities of 'refinement', 'elegant', and 'gorgeous' of loanwords as higher, whereas they had tendency to evaluate the sensibilities of 'familiar' and 'stability' of Hangul as higher or similar.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.183-189
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• 2019

The purpose of this study is to investigate the effects of mobile phone use on university students' English reading achievement and consider the value and effectiveness of mobile phones as a learning tool. The study's subjects were students from a university in Seoul, who were divided into an experimental group (n=37) and a control group (n=43). The experimental group used various mobile phone functions-such as searching, recording, taking photos, using mobile apps, and community uploads-in reading class. The control group, on the other hand, focused on students' presentations and the professor's explanations. Two achievement tests and an open-ended questionnaire were administered. The results revealed that the experimental group scored higher than the control group, which showed a significant difference. In addition, the positive impacts of mobile phone use as reported in the questionnaire were interest and motivation, self-directed learning, and continuous learning beyond time and space. In order to foster efficient second language teaching and learning, learners and also teachers must be aware of the potential value of mobile phones as a learning tool. To encourage more active mobile phone use in the classroom, diverse and interesting class activities using mobile phones should be developed.

• The Korean Journal of Emergency Medical Services
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• v.13 no.3
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• pp.71-76
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• 2009

서론(Introduction) : 의학 시뮬레이션(medical simulation)은 교육생 학습과정에서 내재된 위험이 환자에게 가해짐 없이 교육생이 실제적인 환자 상황을 경험할 수 있게 하고 여러 다양한 임상내용이 포함한 상황에 적용될 수 있다. 시뮬레이션 기술의 사용은 의학교육(medical education), 인증서(certification), 면허교부(Licensure)와 의료의 질 형성에 큰 잠재력을 가지고 있다. 복강경 수술, 내시경검사, 전문심장구조술, 응급기도관리와 외상소생을 포함한 다양한 임상시술의 수행에서 시뮬레이션이 교육생의 술기를 달성하고, 측정하고, 유지하는 유효성을 증명하였다 컴퓨터로 조절되는 시뮬레이터는 맥박, 혈압, 호흡, 대화가 가능하고, 중증질환 또는 외상환자의 치료에 필요한 같은 인명구조 시술을 수행할 수 있다. 의학 시뮬레이션은 의사, 간호사, 응급구조사와 응급 진료를 필요로 하는 환자를 치료하는 사람에게 필요하다. 최신 전문심장구조술 과정수업은 전통적인 강의와 제한된 팀 상호작용이 포함된 이틀 과정이다. 우리는 비 영어권 국제 응급구조학생의 전문심장구조술 술기능력을 알아보고, 그것을 미국 응급구조학생과 비교하고자 한다. 목적(Objective) : 이 연구의 목적은 다양한 전문심장구조술 증례 시나리오를 가진 의학 시뮬레이터를 이용하여 미국과 한국의 응급구조 학생의 능력을 비교하는 것이다. 시행 장소(Site Location) : 이 연구는 한국 제주도에 위치한 제주한라대학 스토니브룩 응급의료교육원에서 진행되었다. 학생들의 평가는 스토니브룩에 위치한 스토니브룩 대학 의료원의 한 명의 평가자(Dr. lee)에 의해 수행되었다. 방법(Methods) : 15명의 한국 응급구조학생들은 세 팀으로 무작위로 선정하였다. 5명이 한 팀이 되어 같은 증례의 시나리오를 받았다. 세 가지 시나리오는 : 첫째, 천식지속상태(Status asthmaticus), 둘째, 긴장기흉을 동반한 만성폐쇄성폐질환(COPD with tension penumothorax) 그리고 마지막으로 메가코드(megacode)를 가진 심정지 이다. 세 팀을 각각 그리고 기본인명구조술(BLS)과 전문심장구조술(ACLS)과정을 마친 미국 응급구조학생들과 비교하였다. 15명의 미국 응급구조학생들 또한 세 팀으로 무작위로 선정하였다. 이 응급구조 학생들은 플러싱병원 의료원 소속으로 그곳에서 이 연구에 참여할 뿐만 아니라 지속적인 의학교육(CME)이수를 받았다. 이들에게도 같은 세 가지 증례의 시나리오가 주어졌고 Dr lee는 총 여섯 팀을 평가하였다(한국 세 팀과 미국 세팀). 결과(Results) : 양 국가의 모든 15명의 학생이 의학시뮬레이터를 사용하여 전문심장구조술 메가코드시험을 포함한 시험에 모두 통과하였다. 비록 학생들을 무작위로 세 팀으로 나누었지만 한 팀이 이 모든 세 증례에서 다른 팀보다 뛰어났다. 제주한라대학 2번 팀은 더 나은 기도관리, 리듬인식과 임상술기를 가진 모든 중요한 활동을 얻기에서 우수했다. 그들은 핵심요구사항을 90% 이상 충족시겼다. 한국의 2번팀(G2K)은 메가코드에서 기도개방, 호흡평가, 순환징후 그리고 흉부압박수와 같은 신체검진 술기에서도 탁월했다. 게다가 다른 팀과 비교 시 리듬인식, 약물지식과 임상술기에서도 높은 점수를 받았으며 2번팀(G2K)이 6팀 중에 가장 뛰어나게 역활수행을 하였다. 결론(Conclusion) : 이 비교 연구에서 한국학생과 미국학생간에 전문심장구조술 메가코드 시험의 통과율에는 차이가 없었다. 그러나 미국학생은 세 팀 사이에 더 적은 변이로 더 일괄된 점수를 받았다. 한국학생들도 모든 세 가지 증례를 통과하였지만 이 세 팀은 미국학생 팀보다 점수에서 더 큰 변이를 보였다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.527-534
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• 2016

The emergent information society requires the talent for creative thinking based on problem-solving skills and comprehensive thinking rather than simple memorization. Therefore, the Korean curriculum has also changed into the direction of the creative thinking through increasing short-answer questions that can determine the overall thinking of the students. However, their scoring results are a little bit inconsistency because scoring short-answer questions depends on the subjective scoring of human raters. In order to alleviate this point, an automated scoring system using a machine learning has been used as a scoring tool in overseas. Linguistically, Korean and English is totally different in the structure of the sentences. Thus, the automated scoring system used in English cannot be applied to Korean. In this paper, we introduce an automated scoring system for Korean short-answer questions using predictability and unanimity. We also verify the practicality of the automatic scoring system through the correlation coefficient between the results of the automated scoring system and those of human raters. In the experiment of this paper, the proposed system is evaluated for constructed-response items of Korean language, social studies, and science in the National Assessment of Educational Achievement. The analysis was used Pearson correlation coefficients and Kappa coefficient. Results of the experiment had showed a strong positive correlation with all the correlation coefficients at 0.7 or higher. Thus, the scoring results of the proposed scoring system are similar to those of human raters. Therefore, the automated scoring system should be found to be useful as a scoring tool.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.125-132
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• 2022

Sentence compression is a natural language processing task that generates concise sentences that preserves the important meaning of the original sentence. For grammatically appropriate sentence compression, early studies utilized human-defined linguistic rules. Furthermore, while the sequence-to-sequence models perform well on various natural language processing tasks, such as machine translation, there have been studies that utilize it for sentence compression. However, for the linguistic rule-based studies, all rules have to be defined by human, and for the sequence-to-sequence model based studies require a large amount of parallel data for model training. In order to address these challenges, Deleter, a sentence compression model that leverages a pre-trained language model BERT, is proposed. Because the Deleter utilizes perplexity based score computed over BERT to compress sentences, any linguistic rules and parallel dataset is not required for sentence compression. However, because Deleter compresses sentences only considering perplexity, it does not compress sentences by reflecting the linguistic information of the words in the sentences. Furthermore, since the dataset used for pre-learning BERT are far from compressed sentences, there is a problem that this can lad to incorrect sentence compression. In order to address these problems, this paper proposes a method to quantify the importance of linguistic information and reflect it in perplexity-based sentence scoring. Furthermore, by fine-tuning BERT with a corpus of news articles that often contain proper nouns and often omit the unnecessary modifiers, we allow BERT to measure the perplexity appropriate for sentence compression. The evaluations on the English and Korean dataset confirm that the sentence compression performance of sentence-scoring based models can be improved by utilizing the proposed method.

• Journal of Information Management
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• v.41 no.2
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• pp.31-46
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• 2010

Now named-entity recognition(NER) as a part of information extraction has been used in the fields of information retrieval as well as question-answering systems. Unlike words, named-entities(NEs) are generated and changed steadily in documents on the Web, newspapers, and so on. The NE generation causes an unknown word problem and makes many application systems with NER difficult. In order to alleviate this problem, this paper proposes a new feature generation method for machine learning-based NER. In general features in machine learning-based NER are related with words, but entities in named-entity dictionaries are related to phrases. So the entities are not able to be directly used as features of the NER systems. This paper proposes an encoding scheme as a feature generation method which converts phrase entities into features of word units. Futhermore, due to this scheme, entities with semantic information in WordNet can be converted into features of the NER systems. Through our experiments we have shown that the performance is increased by about 6% of F1 score and the errors is reduced by about 38%.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.1-10
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• 2017

Longitudinal data of Korean child academic achievement have been used to find the significant exploratory variables under the assumption of independent repeated measured data. Using the exploratory variables in previous research works, we analyze the linear mixed model incorporating the fixed and random effects for child academic achievement to detect the significant exploratory variables. Korea welfare panel study data observed three times between 2006 and 2012 by additional survey for children. The child academic achievement is evaluated by the sum of academic achievements of Korean, English and Mathematics. We also investigate the multicollinearity and the missing mechanism and select some popular correlation matrices to analyze the linear mixed model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.53-60
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• 2020

Sentiment analysis is the automated process of understanding attitudes and opinions about a given topic from written or spoken text. One of the sentiment analysis approaches is a dictionary-based approach, in which a sentiment dictionary plays an much important role. In this paper, we propose a method to automatically generate Korean sentiment lexicon from the well-known English sentiment lexicon called VADER (Valence Aware Dictionary and sEntiment Reasoner). The proposed method consists of three steps. The first step is to build a Korean-English bilingual lexicon using a Korean-English parallel corpus. The bilingual lexicon is a set of pairs between VADER sentiment words and Korean morphemes as candidates of Korean sentiment words. The second step is to construct a bilingual words graph using the bilingual lexicon. The third step is to run the label propagation algorithm throughout the bilingual graph. Finally a new Korean sentiment lexicon is generated by repeatedly applying the propagation algorithm until the values of all vertices converge. Empirically, the dictionary-based sentiment classifier using the Korean sentiment lexicon outperforms machine learning-based approaches on the KMU sentiment corpus and the Naver sentiment corpus. In the future, we will apply the proposed approach to generate multilingual sentiment lexica.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.7-13
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• 2020

With the recent World Health Organization (WHO) Declaration of Pandemic for Coronavirus Disease-19 (COVID-19), COVID-19 is a global concern and many deaths continue. To overcome this, there is an increasing need for sharing information between countries and countermeasures related to COVID-19. However, due to linguistic boundaries, smooth exchange and sharing of information has not been achieved. In this paper, we propose a Neural Machine Translation (NMT) model specialized for the COVID-19 domain. Centering on English, a Transformer based bidirectional model was produced for French, Spanish, German, Italian, Russian, and Chinese. Based on the BLEU score, the experimental results showed significant high performance in all language pairs compared to the commercialization system.