• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.23-50
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• 2022

Source memory is a cognitive process that combines the representation of the origin of the episodic experience with an item. By studying this daily process, researchers have made fundamental discoveries that make up the foundation of brain and behavior research, such as executive function and binding. In this paper, we review and conduct a bibliometric analysis on source memory papers published from 1989 to 2020. This review is based on keyword co-occurrence networks and author citation networks, providing an in-depth overview of the development of source memory research and future directions. This bibliometric analysis discovers a change in the research trends: while research prior to 2010 focused on individuality of source memory as a cognitive function, more recent papers focus more on the implication of source memory as it pertains to connectivity between disparate brain regions and to social neuroscience. Keyword network analysis shows that aging and executive function are continued topics of interest, although frameworks in which they are viewed have shifted to include developmental psychology and meta memory. The use of theories and models provided by source memory research seem essential for the future development of cognitive enhancement tools within and outside of the field of Psychology.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.615-625
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• 2020

The document-term frequency matrix is a term extracted from documents in which the group information exists in text mining. In this study, we generated the document-term frequency matrix for document classification according to research field. We applied the traditional term weighting function term frequency-inverse document frequency (TF-IDF) to the generated document-term frequency matrix. In addition, we applied term frequency-inverse gravity moment (TF-IGM). We also generated a document-keyword weighted matrix by extracting keywords to improve the document classification accuracy. Based on the keywords matrix extracted, we classify documents using a deep neural network. In order to find the optimal model in the deep neural network, the accuracy of document classification was verified by changing the number of hidden layers and hidden nodes. Consequently, the model with eight hidden layers showed the highest accuracy and all TF-IGM document classification accuracy (according to parameter changes) were higher than TF-IDF. In addition, the deep neural network was confirmed to have better accuracy than the support vector machine. Therefore, we propose a method to apply TF-IGM and a deep neural network in the document classification.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.196-202
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• 2023

Large Language Models (LLM)의 출현은 자연어 처리 분야의 연구 패러다임을 전환시켰다. LLM의 핵심적인 성능향상은 지시어 튜닝(instruction-tuning) 기법의 결과로 알려져 있다. 그러나, 현재 대부분의 연구가 영어 중심으로 진행되고 있어, 다양한 언어에 대한 접근이 필요하다. 본 연구는 한국어 지시어(instruction-following) 모델의 개발 및 최적화 방법을 제시한다. 본 연구에서는 한국어 지시어 데이터셋을 활용하여 LLM 모델을 튜닝하며, 다양한 데이터셋 조합의 효과에 대한 성능 분석을 수행한다. 최종 결과로 개발된 한국어 지시어 모델을 오픈소스로 제공하여 한국어 LLM 연구의 발전에 기여하고자 한다.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.331-341
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• 2017

This paper aims to analyze English vocabulary of the North Korean textbooks diachronically using the constructed English textbook corpus. The North Korea English textbooks attained from Information Center on North Korea of the Ministry of Unification are divided into before and after Kim Jong-Il era for the year of 1996 in which the curriculum revision has been conducted. They are stored as text files to analyse vocabularies using WordSmith Tools 7.0. The vocabulary size of the revised textbooks increased after the curriculum reorganization, but the number of vocabulary types and vocabulary diversity decreased. After the curriculum revision, it was found that lots of vocabulary related to the establishment of the Kim Jong-Il system appeared as the keyword. It was also found that some vocabularies reflected the economic and social life of North Korea. In addition, through comparison of the 100 high-frequency word list and keywords, it can be concluded that the vocabulary of the English textbooks of North Korea is gradually changing into communicative contents from contents related with written language.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.4
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• pp.125-145
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• 2016

This study aimed at finding theological trends of Calvin Theological Journal by analyzing Library of Congress Subject Headings (LCSH). The study performed the time-series analysis and the analysis of distinctive terms by examining the main authors and the subject headings of the articles published in Calvin Theological Journal during 45 years. We also proposed a new method of dividing the analysis period with the change of authors and subject headings. In the analysis results, the 18 main authors had the three clusters and shared Calvin and the Reformed Theology, the Bible. The reformed characteristics were shown in the first and second period, but the reformed theology was at the margins. The frequency of Calvin became small in the third period, the frequency of the reformed theology became bigger than before, but it was at the perimeters. Literary criticism was clustered independently. There were lots of the terms of the reformed theology in the analysis of the distinctive terms in all three periods and especially in the 2-1 period science and religion were included as the distinctive terms. Therefore, the theological tendency of the Calvin Theological Journal seemed the reformed theology and Old Testament.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.694-695
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• 2007

한국어 문장의 구성은 명사, 동사와 같은 내용어와 조사, 어미와 같은 기능어로 크게 나눌 수있다. 문장의 핵심적인 의미 전달은 내용어에 의해 이루어지며, 한국어 명사구의 경우 잦은 기능어의 생략으로 명사 나열에 의한 복합 명사가 발생된다. 이렇게 발생되는 복합 명사를 구성하는 단위 명사들은 일부 문장 성분을 생략시켜 발생된 것으로, 생략 성분의 복원에 의해 본래의 문장 형태를 추정할 수 있다. 한국어 복합 명사의 경우, 생략되는 문장 성분은 대부분 접사, 조사와 같은 기능어로 국한되며, 기능어의 복원은 단위 명사 간의 격 관계와 의미 관계를 분석하여 이루어질 수 있다. 본 논문에서는 단위 명사간의 보-술 관계를 이용하여 복합 명사를 구성하는 단위 명사 간의 의존 관계를 추정하고, 추정된 의존 관계에 의해 생략된 격조사와 용언화 접사를 복원하는 방법을 제안한다. 구조 분석에서 사용되는 의미 격틀에 의해 결정되는 격 관계는 격조사와 용언화 접사의 복원을 결정하며, 올바른 본래의 문장 표현 복원을 위해 관형격 조사와 관형격 어미를 비롯한 특별한 형태의 복원은 통계 정보와 휴리스틱 규칙으로 결정한다.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.595-602
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• 2008

The focused word of each sentence is a help in recognizing and understanding spoken Korean. To find the method of focused word spotting at spoken speech signal, we made an analysis of the average and variance of Fundamental Frequency and the average energy extracted from a focused word and the other words in a sentence by experiments with the speech data from 100 spoken sentences. The result showed that focused words have either higher relative average F0 or higher relative variances of F0 than other words. Our findings are to make a contribution to getting prosodic characteristics of spoken Korean and keyword extraction based on natural language processing.

• The Journal of the Korea Contents Association
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• v.6 no.10
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• pp.9-16
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• 2006

In this paper, the acoustic model output of CSR(Continuous Speech Recognition) was used to analyze news data News database used in this experiment was consisted of 2,093 articles. Due to the low efficiency of language model, conventional Korean CSR is not appropriate to the analysis of news data. This problem could be handled successfully by introducing post-processing work of recognition result of acoustic model. The acoustic model more robust than language model in Korean environment. The result of post-processing work was made into KIF(Keyword information file). When threshold of acoustic model's output level was 100, 86.9% of whole target morpheme was included in post-processing result. At the same condition, applying length information based normalization, 81.25% of whole target morpheme was recognized. The purpose of normalization was to compensate long-length morpheme. According to experiment result, 75.13% of whole target morpheme was recognized KIF(314MB) had been produced from original news data(5,040MB). The decrease rate of absolute information met was approximately 93.8%.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.333-342
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• 2021

In this study, we analyzed the vocabulary, lemmas, keywords, and n-grams in 『Treasure Island』 to identify certain linguistic features of this British-American children's adventure novel. The current study found that, contrary to the popular claim that frequently-used words are important and essential to a story, the set of frequently-used words in 『Treasure Island』 were mostly function words and proper nouns that were not directly related to the plot found in 『Treasure Island』. We also ascertained that a list of keywords using a statistical method making use of a corpus program was not good enough to surmise the story of 『Treasure Island』. However, we managed to extract 30 keywords through the first quantitative keyword analysis and then a second qualitative keyword analysis. We also carried out a series of n-gram analyses and were able to discover lexical bundles that were preferred and frequently used by the author of 『Treasure Island』. We hope that the results of this study will help spread this knowledge among British-American children's literature as well as to further put forward corpus stylistic theory.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.921-935
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• 2017

The purpose of this research is to investigate trends of science educational researches on underrepresented students by scrutinizing Korean science education research literatures. For this particular purpose, literatures on underrepresented students were extracted from both listed and candidate journals for KCI and theses from 1984 to February 2017, and analyzed criteria such as source, year of publication, design, method, and content of research. A total of 125 papers from journals and 147 theses were extracted. In these researches, 61%, 20%, 6% were about students with disability, underachievers, and North Korean defector students respectively. The ratio of the researches on other underrepresented students such as multicultural, low income families, students who are from rural areas, and other underrepresented students were less than 5%. According to the year of publication, it was found that the number of research papers on underrepresented students increased continuously by a single digit from 1984 by focusing on the students with disability and underachievers. After that, from around 2008, it showed a rapid increase and researches on underrepresented students carried out more than 20 times annually. With regards to research design, there were 58% quantitative, 28% qualitative and 14% hybrid research design. Through analysis of research methods, we found that 30% of experimental research, 22% of interpretive research, 20% of correlation analysis, and 14% of survey research. After going through the characteristics of the research contents by visualizing the relationship between the research groups and the keywords that were extracted, it was found that even though the science education researches on underrepresented students have various contents, there were no keywords that were researched continuously and intensively in this area. The structural relationship between the keywords and each research group on underrepresented students showed that 'academic achievement' is the keyword with the highest degree of mediateness and connectedness.