• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.596-607
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• 2017

This study examined the method for adding related to "stomach neoplasms" as filters to the Medical Subject Headings (MeSH) for search as well as a method for improving the search efficiency through a word-proximity search by measuring the distance of co-occurring terms. A total of 8,625 articles published between 2007 and 2016 with the major topic terms "stomach neoplasms" were downloaded from PubMed article titles. The vocabulary to be added to the MeSH for search were analyzed. The search efficiency was verified by 277 articles that had "Stomach Neoplasms" indexed as MEDLINE MeSH in KoreaMed. As a result, 973 terms were selected as the candidate vocabulary. "Gastric Cancer" (2,780 appearances) was the most frequent term and 7,376 compound words (88.51%) combined the histological terms of "stomach" and "neoplasm", such as "gastric adenocarcinoma" and "gastric MALT lymphoma". A total of 5,234 compounds words (70.95%), in which the co-occurring distance was two words, were found. The matching rate through the MEDLINE MeSH and KoreaMed MeSH Indexer was 209 articles (75.5%). The search efficiency improved to 263 articles (94.9%) when the search filters were added, and to 268 articles (96.7%) when the 13 word-proximity search technique of the co-occurring terms was applied. This study showed that the use of a thesaurus as a means of improving the search efficiency in a natural language search could maintain the advantages of controlled vocabulary. The search accuracy can be improved using the word-proximity search instead of a Boolean search.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.79-84
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• 2008

많은 사용자들의 참여로 구축된 집단 지성을 이용한 지식 검색 서비스에서 사용자가 원하는 답변을 빨리 찾고자 하는 요구가 증가하고 있다. 기존의 연구에서 조회 수, 추천 수, 답변 수와 같은 비텍스트 정보가 답변을 평가하는데 좋은 자질임이 증명되었고, 신뢰도를 추정할 수 있는 여러 종류의 단어 사전을 이용하여 답변의 좋고 나쁨을 평가할 수 있는 연구도 진행되었다. 하지만, 조회 수, 추천 수, 답변 수와 같은 비텍스트 정보는 사용자 조작이 간단하여 지속적으로 관리를 해야 하며, 신뢰도를 추정할 수 있는 단어는 지속적으로 보강되어야 한다. 본 논문에서는 이러한 문제점을 해결하고자 동시출현 자질을 이용한 질문과 답변의 유사성을 활용하여 집단 지성에서 사용자의 활동을 분석하여 사용자의 명성을 평가하는 방법을 제안한다. 사용자의 명성을 계산할 수 있다면 조회 수와 추천 수가 많지 않은 답변의 신뢰도도 비교적 정확하게 추정할 수 있다. 이를 위해 우리는 PageRank 알고리즘을 수정하여 사용자 명성을 계산한다. 네이버 지식iN의 문서로 실험한 결과, 기존 정답 선택률을 보완할 수 있는 결과를 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.88-90
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• 2022

영어 교육 시장이 증대되면서 영어 학습을 효과적으로 지원하는 다양한 학습 시스템들이 개발되고 있다. 영어문장을 구성하는 기본적인 단위는 어휘로 문장 전체의 의미를 파악하기 위해서는 어휘의 의미를 이해하는 것이 필수적이다. 따라서 영어 어휘 능력 향상을 위한 다양한 영어 어휘 학습 시스템들이 개발되고 있으나, 어휘가 사용되는 문맥을 고려하거나 동시에 학습하면 효과적인 어휘 등 어휘 학습에 효과적인 교수학습 방법의 원리가 적용된 영어 어휘 학습 시스템에 대한 연구는 미비한 상황이다. 본 논문에서는 n 개의 영어 단어가 하나의 그룹으로 동시에 제시되면서 그 n개의 영어 단어가 모두 포함된 예문을 제공하는 지능형 영어 어휘 학습 시스템을 설계한다. 임의로 n 개의 영어 어휘가 주어졌을 경우 문맥에 맞게 영어 예문을 자동으로 생성하는 지능형 영어 문장 생성 모델이 본 연구의 핵심이다. 또한, 어휘 능력 평가에서 기존 어휘 학습 시스템과 같이 단순히 어휘를 얼마나 암기하고 있는지에 대한 평과 결과만을 제시하는 것이 아니라, 그룹별 취약 어휘 분석을 통해 효과적인 그룹 어휘 선택 규칙을 파악할 수 있는 기반을 마련하고자 한다. 본 논문에서 제안한 지능형 영어 어휘 학습 시스템을 통해 영어 어휘 학습자들의 학습 능력 향상에 도움이 될 것으로 기대한다.

• Korean Journal of Environment and Ecology
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• v.31 no.1
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• pp.30-41
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• 2017

Mammals in Korea have been studied in various fields such as animal science, veterinary medicine, laboratory animal science, ecology, and genetics. As the importance of biodiversity has been emphasized recently, conservation and management of mammals have attracted much public attention. However, in spite of such an increase in scientific research and public interest, it is still difficult to find a report or summary to grasp the trend of mammalian research in Korea. The purpose of this study is to provide the basic data for future plans of the detailed research area and the related policies by grasping the research trends of mammals in Korea. Using text-ming and co-word analysis, we analyzed 392 mammalian research papers published in Korean national journals as of 2015. Our results showed that the number of mammalian research papers published in Korea has gradually increased and that the research target species have also become increasingly diverse. The major research areas identified through text-mining and co-word analysis are (1) evolution/phylogenetics/genetics, (2) environmental science/ecology, (3) embryology/reproductive biology/cell biology, (4) veterinary medicine related to parasites, (5) parasitology related to rodents, (6) bacteriology/virology, (7) anatomy/cell biology/laboratory animal science, (8) veterinary science related to morphology and anatomy, (9) animal science, (10) marine mammalogy, and (11) Chiroptera (bat) research. Environmental science/ecology has been the most active field among the 11 research areas in recent times, and the proportion of research has increased sharply compared to the past. Environmental science/ecology is the core of biodiversity conservation, and as the importance of biodiversity has been emphasized in recent years, researchers' interest in mammal ecology appears to have increased. We expect that the results of this study will be useful for future research plan and related policies on mammals in Korea.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.247-264
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• 2021

This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.

• Korean Journal of Cognitive Science
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• v.15 no.2
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• pp.23-31
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• 2004

Latent Semantic Analysis (LSA Landauer ＆ Dumais, 1997) is a technique to represent the meanings of words using co-occurrence information of words appearing in he same context, which is usually a sentence or a document. In LSA, a word is represented as a point in multidimensional space where each axis represents a context, and a word's meaning is determined by its frequency in each context. The space is reduced by singular value decomposition (SVD). The present study elaborates upon LSA for use of representation of ambiguous words. The proposed LSA applies rotation of axes in the document space which makes possible to interpret the meaning of un. A simulation study was conducted to illustrate the performance of LSA in representation of ambiguous words. In the simulation, first, the texts which contain an ambiguous word were extracted and LSA with rotation was performed. By comparing loading matrix, we categorized the texts according to meanings. The first meaning of an ambiguous wold was represented by LSA with the matrix excluding the vectors for the other meaning. The other meanings were also represented in the same way. The simulation showed that this way of representation of an ambiguous word can identify the meanings of the word. This result suggest that LSA with axis rotation can be applied to representation of ambiguous words. We discussed that the use of rotation makes it possible to represent multiple meanings of ambiguous words, and this technique can be applied in the area of web searching.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.411-438
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• 2024

This study aims to identify and comprehensively view health information-related research trends using a bibliometric analysis. To this end, 1,193 papers from 2002 to 2023 related to "health information" were collected through the Korea Citation Index (KCI) database and analyzed in diverse aspects: research trends by period, academic fields, intellectual structure, and keyword changes. Results indicated that the number of papers related to health information continued to increase and has been decreasing since 2021. The main academic fields of health information-related research included "biomedical engineering," "preventive medicine/occupational environmental medicine," "law," "nursing," "library and information science," and "interdisciplinary research." Moreover, a co-word analysis was performed to understand the intellectual structure of research related to health information. As a result of applying the parallel nearest neighbor clustering (PNNC) algorithm to identify the structure and cluster of the derived network, four clusters and 17 subgroups belonging to them could be identified, centering on two conglomerates: "medical engineering perspective on health information" and "social science perspective on health information." An inflection point analysis was attempted to track the timing of change in the academic field and keywords, and common changes were observed between 2010 and 2011. Finally, a strategy diagram was derived through the average publication year and word frequency, and high-frequency keywords were presented by dividing them into "promising," "growth," and "mature." Unlike previous studies that mainly focused on content analysis, this study is meaningful in that it viewed the research area related to health information from an integrated perspective using various bibliometric methods.

• Explosives and Blasting
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• v.40 no.3
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• pp.1-18
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• 2022

Due to the advent of the hydrogen economy era, various studies are being conducted to transport and store hydrogen, and the risk of hydrogen explosion is emerging. In order to figure out the new technology related to hydrogen energy, it is necessary to figure out the overall research trends related to various hydrogen energy at home and abroad. In this study, a bibliometric analysis using VOSViewer for the papers published in the international journal was conducted. From the analysis in different time period using the keywords including hydrogen explosion, hydrogen pipeline, and hydrogen storage, it was found that there were frequent paper publications using numerical analysis simulation. It is also found that more and more researches on safety and hydrogen explosion in hydrogen storage and hydrogen pipeline transportation have been conducted in 2011-2022 compared to those in 2000-2010.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.269-294
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• 2024

This study analyzed the news media coverage of QRcodes in Korea over a 16-year period (2008 to 2023). A total of 13,335 articles were extracted from the Korea Press Foundation's BigKinds. A quantitative and content analysis was conducted on the news frames. The results indicated that the quantity of news coverage has increased. The greatest quantity of news coverage was observed in 2020, and the most frequently discussed topic in the news was 'IT_Science'. The results of the keyword analysis indicated that the primary words were 'QRcode', 'smartphone', 'service', 'application', and 'payment'. The news media primarily focused on the QRcode's ability to provide instant access and recognition technology. This study demonstrates that advanced information and communication technologies and the increased prevalence of mobile devices have led to a rise in the utilization of QRcodes. Furthermore, QRcodes have become a significant information media in contemporary society.