• Explosives and Blasting
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• v.40 no.1
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• pp.1-16
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• 2022

In order to develop effective and safe blasting techniques or to introduce foreign advanced blasting techniques to domestic industry, the analysis of research trend in blasting field in the world is essential. In generally, such a research trend analysis was carried out for limited number of published papers. In this study, a bibliometric analysis was performed using VOSviewer for the overall papers published in international journals to figure out the variation of research trend in blasting area. From the keyword analysis, it was found that the number of published papers and the number of overall keywords was limited in the 2000s. Since 2010, the number of published papers was increased rapidly and the keywords were diversified with the introduction of artificial intelligence(AI). The keyword analysis for 2017~2021 showed that various hybrid AI techniques were actively applied in the evaluation of blasting effect.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.1
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• pp.207-228
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• 2013

Due to the advance of scholarly communication, the field of open access has been studied over the last decade. The purpose of this study is to analyze and demonstrate the field of open access via co-word analysis. The data set was collected from Web of Science citation database during the period from January 1998 to July 2012 using the Topic category. A total of 479 journal articles were retrieved and 8,643 noun keywords were extracted from the titles and abstracts. In order to achieve the purpose of this study, network analysis, clustering analysis and multidimensional scaling mapping were used to examine the domain and the sub-domains of open access field. 18 clusters in the network analysis are recognized and 4 clusters are shown in the map of multidimensional scaling. In addition, the centrality analysis in the weighted networks was used to explore the significant keywords in this field. The results of this study are expected to demonstrate and guide the intellectual structure and new approaches of open access field.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.121-138
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• 2017

As information users seek multimedia with a wide variety of information needs, information environments for multimedia have been developed drastically. More specifically, as seeking multimedia with emotional access points has been popular, the needs for indexing in terms of abstract concepts including emotions have grown. This study aims to analyze the index terms extracted from Getty Image Bank. Five basic emotion terms, which are sadness, love, horror, happiness, anger, were used when collected the indexing terms. A total 22,675 index terms were used for this study. The data are three sets; entire emotion, positive emotion, and negative emotion. For these three data sets, co-word occurrence matrices were created and visualized in weighted network with PNNC clusters. The entire emotion network demonstrates three clusters and 20 sub-clusters. On the other hand, positive emotion network and negative emotion network show 10 clusters, respectively. The results point out three elements for next generation of multimedia retrieval: (1) the analysis on index terms for emotions shown in people on image, (2) the relationship between connotative term and denotative term and possibility for inferring connotative terms from denotative terms using the relationship, and (3) the significance of thesaurus on connotative term in order to expand related terms or synonyms for better access points.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.537-541
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• 2006

최근 특정 주제의 지적 구조를 파악하기 위한 저자 동시인용분석, 동시단어분석, 서지결합법 등 계량정보분석에 대한 연구가 활발히 진행되고 있다. 그러나 국내의 경우 계량정보분석 기법을 활용한 정보 유통 프레임웍을 갖추고 있는 연구기관이나 대학이 아직 없는 실정이다. 그 이유는 특허나 과학문헌에 대한 인용정보를 보유한 곳이 없고, 거대 인용정보 행렬을 계산하기 위한 컴퓨팅 자원을 확보하지 못하고 있기 때문이다. 본 연구는 미국 특허 데이터베이스를 대상으로 인용 피인용 행렬을 생성한 후, 클러스터 컴퓨터를 사용하여 동시인용과 서지결합빈도를 계산하고 그 결과를 이용자에게 제공하는 정보 유통 서비스 모델을 제시하고자 한다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.3
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• pp.101-118
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• 2014

Given the context of rapid developing ubiquitous computing environment, it is imperative for users to search and use images based on affective meanings. However, it has been difficult to index affective meanings of image since emotions of image are substantially subjective and highly abstract. In addition, utilizing low level features of image for indexing affective meanings of image has been limited for high level concepts of image. To facilitate the access points of affective meanings of image, this study aims to utilize user-provided responses of images. For a data set, emotional words are collected and cleaned from twenty participants with a set of fifteen images, three images for each of basic emotions, love, sad, fear, anger, and happy. A total of 399 unique emotion words are revealed and 1,093 times appeared in this data set. Through co-word analysis and network analysis of emotional words from users' responses, this study demonstrates expanded word sets for five basic emotions. The expanded word sets are characterized with adjective expression and action/behavior expression.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.431-453
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• 2022

Hate is a collective expression of exclusivity toward others and it is fostered and reproduced through false public perception. This study aims to explore the objects and issues of hate discussed in our society using text mining techniques. To this end, we collected 17,867 news data published from 1990 to 2020 and constructed a co-word network and cluster analysis. In order to derive an explicit co-word network highly related to hate, we carried out sentence split and extracted a total of 52,520 sentences containing the words 'hate', 'prejudice' and 'discrimination' in the preprocessing phase. As a result of analyzing the frequency of words in the collected news data, the subjects that appeared most frequently in relation to hate in our society were women, race, and sexual minorities, and the related issues were related laws and crimes. As a result of cluster analysis based on the co-word network, we found a total of six hate-related clusters. The largest cluster was 'genderphobic', accounting for 41.4% of the total, followed by 'sexual minority hatred' at 28.7%, 'racial hatred' at 15.1%, 'selective hatred' at 8.5%, 'political hatred' accounted for 5.7% and 'environmental hatred' accounted for 0.3%. In the discussion, we comprehensively extracted all specific hate target names from the collected news data, which were not specifically revealed as a result of the cluster analysis.

• Journal of the Korean Society for information Management
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• v.31 no.3
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• pp.293-311
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• 2014

This study explored the research trends on Twitter in Korea by informetric methods. All 539 articles on Twitter published from 2009 to the April of 2014 were obtained from the KCI. Only article titles, abstracts, and keywords by authors were used in analysis. Academic journals in many different disciplines where Twitter articles were produced were analysed by profiling, and then, the subject areas of researches on Twitter were analysed by co-word analysis. The results of this study showed that Twitter-related papers were published in as many as 53 disciplines with journalism, business administration, and computer science to be core fields. It was also found that the core subject areas are political issues and business.

• Proceedings of the Korean Society for Information Management Conference
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• 2001.08a
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• pp.181-184
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• 2001

본 논문에서는 적합한 색인어를 자동으로 추출해 내기 위해 잘 알려진 통계적 기법과 구문분석적 기법을 혼용하였다. 적용결과를 검색효율로 나타내지 않고 각 방법에 따라 추출된 단어들을 실증적으로 보여주어 성능에 대한 판단을 유도하였다. 빈도나 품사가 단독으로 사용된 것보다 동시에 적용된 것이 보다 좋은 결과를 가져왔다.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.123-143
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• 2011

At what point in the process of speech production are gestures involved? According to the Lexical Retrieval Hypothesis, gestures are involved in the lexicalization in the formulating stage. According to the Information Packaging Hypothesis, gestures are involved in the conceptual planning of massages in the conceptualizing stage. We investigated these hypotheses, using the game situation in a TV program that induced the players to involve in both lexicalization and conceptualization simultaneously. The transcription of the verbal utterances was augmented with all arm and hand gestures produced by the players. Coverbal gestures were classified into two types of gestures: lexical gestures and motor gestures. As a result, concrete words elicited lexical gestures significantly more frequently than abstract words, and abstract words elicited motor gestures significantly more frequently than concrete words. The difficulty of conceptualization in concrete words was significantly correlated with the amount of lexical gestures. However, the amount of words and the word frequency were not correlated with the amount of both gestures. This result supports the Information Packaging Hypothesis. Most of all, the importance of motor gestures was inferred from the result that abstract words elicited motor gestures more frequently rather than concrete words. Motor gestures, which have been considered as unrelated to verbal production, were excluded from analysis in many gestural studies. This study revealed motor gestures seemed to be connected to the abstract conceptualization.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.380-382
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• 2019

어의 중의성 문제는 자연어 분석 과정에서 공통적으로 발생하는 문제로 한 가지의 단어 표현이 여러 의미로 해석될 수 있기 때문에 발생한다. 이를 해결하기 위한 어의 중의성 해소는 입력 문장 중 여러 개의 의미로 해석될 수 있는 단어가 현재 문맥에서 어떤 의미로 사용되었는지 분류하는 기술이다. 어의 중의성 해소는 입력 문장의 의미를 명확하게 해주어 정보검색의 성능을 향상시키는데 중요한 역할을 한다. 본 논문에서는 딥러닝을 이용하여 어의 중의성 해소를 수행하며 기존 모델의 단점을 극복하여 입력 문장에서 중의적 단어를 판별하는 작업과 그 단어의 의미를 분류하는 작업을 동시에 수행하는 모델을 제안한다.