• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.239-248
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• 2005

이 논문은 과학영재교육원의 학생 선발과정을 중심으로 과학영재아 판별과정을 분석해보고, 과학영재교육원의 학생 선발과정에서 발생할 수 있는 오류를 점검하여, 이를 보완할 수 있는 개선안을 마련하고자 하는데 목적을 두고 있다. 분석결과 과학, 수학과 관련된 창의적 문제의 지필평가 성적이 선발의 가장 중요한 기준이었다. 지필평가 단계에서 지망분야에 관계없이 과학, 수학 및 창의력검사를 모두 치러야 하는 교육원에 초점을 맞추어 그 점수 활용에 있어서 발생할 수 있는 오류를 점검하였다. A 교육원의 경우 학생 선발은 다단계 평가를 원칙으로 하고 있었으며, 1단계 지필평가에서 과학, 수학, 창의력검사라는 세 과목 시험의 합산점수로서 선발하고 있었다. 이 교육원의 ○○년도 중등과정 응시자 276명을 대상으로 합격자와 불합격자의 지필평가 점수를 분석하여 과학, 수학, 창의력검사의 시행에 오류가 없었는지 점검하였다. 또 이들의 합산에 의한 선발이 의미 있는 결과를 보이는지 분석하였다. 그 결과 과학, 수학, 창의력검사의 상관도분석에서 과학은 수학 및 창의력검사와 유의미한 상관이 있었으나, 수학과 창의력검사는 독립적으로 분석되었다. 또 이들의 합산에 지원분야별 배점으로 계산한 선발은 본래의 취지, 즉 과학, 수학, 창의력에서 모두 우수한 학생을 선발하고자 하는 의도대로 진행되었으나, 판별분석 결과 합격과 불합격자 판별에서 88.1%의 정확도를 보여 다소 오류가 있었음을 발견하였다. 이는 해당년도에 출제된 문제의 난이도 및 시험 과목별 평균점수 차를 고려하지 않아 발생하는 문제로 파악되어져 원점수 대신 표준점수로 변환하여 오류를 보완할 것을 제안한다. 자체와 직접 관련되는 영역으로는 좌반구의 측두엽과 전두엽 부분이 관찰되었다. 특히 한국어 어말어미 산출시 나타나는 형태점화 양상과 관련된 대뇌영역으로 발견된 브로카 영역에서의 활성화는 어미 변환과 관련된 영역이라기보다는 산출시 관련되는 articulation, motor coordinate관련 영역으로 추정되고, 측두엽의 활성화는 형태소, 의미 관련 지식의 data base로 추정된다. 또한 우반구 전두엽 부분에서 관찰된 활성화는 억제관련 영역으로 짐작된다.러한 동물실험이 그 기초를 제공해 줄 수 있을 것이다. 또한 행동성향 및 기억의 종류에 따른 약물효과의 차이는 기억과 관련된 질병인 알츠하이머 환자에 있어 개개인에게 맞는 적절한 특징적인 치료약물이 존재할 것이라는 가능성을 제공해줄 뿐만 아니라 학습과 기억력 증진 효과를 기대해 볼 수 있을 것이라고 생각된다. 및 지역산업발전의 기획${\sim}$조정기구로서, 선진국의 지역발전기구(Regional Development Agency : RDA)인 지역전략산업기획단이 2002년도부터 산업자원부와 9개 시도에 의해 설립되어 지역네트워크의 활성화와 클러스터의 형성 촉진을 하게 되었고 2004년도에는 13개시도로 확대${\sim}$운영되고 있고, 지역특화사업(H/W)과 지역산업기술개발과제(S/W)와 함께 패케지 형태로 지원되며, 주요역할은 크게 지역산업의 정책기획 분야와 평가관리, 지역혁신역량 조사 및 DB구축 등으로 구분된다. 그중에서도 권역별, 지역별, 지역산업진흥사업 육성과 중장기 산업발전계획을 수립하기 위하여 지역혁신역량을 바탕으로 한 지역 Technol

• 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.8 no.11
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• pp.441-448
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• 2019

Previous researches on automatic spacing of Korean sentences has been researched to correct spacing errors by using n-gram based statistical techniques or morpheme analyzer to insert blanks in the word boundary. In this paper, we propose an end-to-end automatic word spacing by using deep neural network. Automatic word spacing problem could be defined as a tag classification problem in unit of syllable other than word. For contextual representation between syllables, Bi-LSTM encodes the dependency relationship between syllables into a fixed-length vector of continuous vector space using forward and backward LSTM cell. In order to conduct automatic word spacing of Korean sentences, after a fixed-length contextual vector by Bi-LSTM is classified into auto-spacing tag(B or I), the blank is inserted in the front of B tag. For tag classification method, we compose three types of classification neural networks. One is feedforward neural network, another is neural network language model and the other is linear-chain CRF. To compare our models, we measure the performance of automatic word spacing depending on the three of classification networks. linear-chain CRF of them used as classification neural network shows better performance than other models. We used KCC150 corpus as a training and testing data.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.39-47
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• 2021

The construction of high-quality input features through effective segmentation is essential for increasing the sentence comprehension of a language model. Improving the quality of them directly affects the performance of the downstream task. This paper comparatively studies the segmentation that effectively reflects the linguistic characteristics of Korean regarding word and sentence classification. The segmentation types are defined in four categories: eojeol, morpheme, syllable and subchar, and pre-training is carried out using the RoBERTa model structure. By dividing tasks into a sentence group and a word group, we analyze the tendency within a group and the difference between the groups. By the model with subchar-level segmentation showing higher performance than other strategies by maximal NSMC: +0.62%, KorNLI: +2.38%, KorSTS: +2.41% in sentence classification, and the model with syllable-level showing higher performance at maximum NER: +0.7%, SRL: +0.61% in word classification, the experimental results confirm the effectiveness of those schemes.

• Cross-Cultural Studies
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• v.50
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• pp.425-451
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• 2018

In this paper, we examined the issues of the discussions on the subject of doublets. In general, as a definition, the use of doublets refer to a pair of words which have a common etymon, but also to a pair of words or grammatical morphemes that have the same meaning and similar forms of the word. In this paper, we have seen that a typical pairing word is a pair of words with a common etymology. Generally speaking, it is possible to divide doublets into subtypes depending on the identified similarities or differences in the meaning or form. The most distant type from the typical type of doublets is a pair of words that do not have a common etymon, but have the same meaning and are similar in form. The second issue about doublets is whether doublets include only words. For example, if some josas (postpositions or particles) have a common etymon, then it is noted that they can be accepted as a kind of doublets. In the case of suffixes, it may be possible to recognize the suffixes as doublets if they have a common etymon. In other words, it is not necessary to recognize the suffixes as doublets because the derivatives which are derived by the suffixes can be accepted as doublets. In the case of endings, it may be possible to recognize a pair of endings which have the same meaning and the common etymon as a doublet. Otherwise, the word forms to which the endings are combined can be accepted likewise as doublets. However, considering the fact that the endings typically in use in the Korean language may have syntactic properties, the endings should be considered as doublets rather than the words which have the endings. Finally, we conclude that there may be some debate as to whether stem doublets or ending doublets belong to a lexical item in the lexicon. It can be said that they are plural underlying forms and may be deserving of further research.

• 재활복지
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• v.21 no.1
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• pp.193-212
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• 2017

The elderly often produce irrelevant speech and get off-topic more easily than the young; the former also has difficulty generating fewer syntactic structures and makes errors of grammatical morphemes. In particular, the elderly might have more difficulty writing since it requires more complex cognitive processes than storytelling. The participants in this study were 32 young people and 32 older people. They were asked to write a short story of Korean fairy tale('Heungbu Nolbu'). The data was analyzed in narrative composition and syntactic structures. The study revealed the following: First, in composition aspects, the elderly group showed significantly lower total number of story grammar and episodes. In addition, the elderly produced more off topic statements. Second, in syntactic aspects, although there was no significant difference in the number of producing complex sentences between two groups, the elderly group generated more inadequate cohesive devices and used fewer relative and adverbial clauses. These findings suggest that the elderly have a tendency to perform tasks by producing more off-topic statements and shows decreasing coherence by using lower number of relative and adverbial clauses. However, this study also uncovers that the elderly were able to write more complex and longer sentences using visual feedback.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.79-88
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• 2019

Machine translation (MT) system, especially Korean-Mongolian MT system, has recently attracted much attention due to its necessary for the globalization generation. Korean and Mongolian have the same sentence structure SOV and the arbitrarily changing of their words order does not change the meaning of sentences due to postpositional particles. The particles that are attached behind words to indicate their grammatical relationship to the clause or make them more specific in meaning. Hence, the particles play an important role in the translation between Korean and Mongolian. However, one Korean particle can be translated into several Mongolian particles. This is a major issue of the Korean-Mongolian MT systems. In this paper, to address this issue, we propose a method to use the combination of UTagger and a Korean-Mongolian particles table. UTagger is a system that can analyze morphologies, tag POS, and disambiguate homographs for Korean texts. The Korean-Mongolian particles table was manually constructed for matching Korean particles with those of Mongolian. The experiment on the test set extracted from the National Institute of Korean Language's Korean-Mongolian Learner's Dictionary shows that our method achieved the accuracy of 88.38% and it improved the result of using only UTagger by 41.48%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.105-112
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• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.4
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• pp.171-181
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• 2018

As the proportion of sports events in the sports industry grows, the official sponsor market for sports events is also increasing. But because official sponsors are limited and expensive, some companies approach sporting events by way of Ambush marketing. This study is to analyze the differences of media exposure between official sponsors and general companies of mega sport events. To accomplish the purpose of the study, we collected text articles and analyzed them from the period of 2016 Rio Olympics, one year before the Olympics and one year after the Olympics. Web crawling was performed using Python for the collection of articles. Morphological and frequency analysis was performed using the KoNLP package and the TM package of statistical program R. In addition, the opinions of the related experts group were gathered to classify the companies or organizations in the media as the Organizing Committees for the Olympic Games(OCOGs), official sponsor, and general companies. As a result of the analysis, 5,220 times appeared related to the OCOGs, 7,845 times appeared related to the official sponsor, and 7,028 times appeared related to general companies. There isn't much difference in the frequency of exposure between official sponsors and general companies. It implies that Ambush marketing is recognized as a strategic marketing technique. The International Olympic Committee(IOC) has to recognize these social phenomena and establish reasonable standards for the marketing activities of official sponsors and general companies. And this study will serve as a basis for fair sponsor activities or marketing activities of sports events.

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.231-248
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• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.