• Journal of Korean Library and Information Science Society
• /
• v.48 no.1
• /
• pp.207-225
• /
• 2017

The aim of this study was to discover various Library and Information Science (LIS) research areas by examining similarities and differences between LIS journals in terms of keyword characteristics. To conduct this study, for the years from 2004 to 2016, the keywords of 6 international journals were downloaded from Scopus database (http://www.scopus.com), and the keywords of 4 Korean journals were downloaded from the RISS database (http://www.riss.co.kr). The characteristics of keywords were investigated by examining frequently used keywords and frequently used distinctive keywords pertaining to international and Korean journals. The distinctive keywords are referred to as the keywords that appear in one domain but not in another. The result of this study indicated the following: a) a frequency analysis of the keywords showed major research themes and unique traits concerning Korea. b) In general, the keywords used in Korean journals frequently reflected the library as a major subject area of research, while keywords used in international journals reflected bibliometrics and information retrieval as major subject areas of research. c) The overarching themes of each created dataset were clearly noticeable in frequently used distinctive keywords. d) Some keywords were bound by a nation or by a region due to their scope of usage. The important implication of this study is that both most frequently used keywords and most frequently used distinctive keywords seemed to adequately represent the LIS subject areas.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.583-586
• /
• 2023

대량 문자서비스를 통한 스팸 문자가 계속 증가하면서 이로 인해 도박, 불법대출 등의 광고성 스팸 문자에 의한 피해가 지속되고 있다. 이러한 문제점을 해결하기 위해 다양한 방법들이 연구되어 왔지만 기존의 방법들은 주로 사전 정의된 키워드나 자주 나오는 단어의 출현 빈도수를 기반으로 스팸 문자를 검출한다. 이는 광고성 문자들이 시스템에서 자동으로 필터링 되는 것을 회피하기 위해 비정상 문자를 조합하여 스팸 문자의 주요 키워드를 의도적으로 변형해 표현하는 경우에는 탐지가 어렵다는 한계가 있다. 따라서, 본 논문에서는 이러한 문제점을 해결하기 위해 딥러닝 기반 객체 탐지 및 OCR 기술을 활용하여 스팸 문자에 사용된 변형된 문자열을 정상 문자열로 복원하고, 변환된 정상 문자열을 문장 수준 이해를 기반으로 하는 자연어 처리 모델을 이용해 스팸 문자 콘텐츠를 분류하는 방법을 제안한다. 그리고 기존 스팸 필터링 시스템에 가장 많이 사용되는 키워드 기반 필터링, 나이브 베이즈를 적용한 방식과의 비교를 통해 성능 향상이 이루어짐을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.11a
• /
• pp.283-286
• /
• 2011

스팸 메시지는 불특정 다수에게 보내지는 광고성 메시지로서 최근 들어 그 양이 증가하고 있는 추세이다. 본 논문에서는 모바일 환경에서의 스팸 메시지 필터링을 위한 시스템을 제안하며 기존 환경에서 자주 사용되었던 키워드 기반 필터링 시스템의 단점을 해결하고자 고안되었다. 베이지안 네트워크를 통해 스팸 메시지들의 패턴을 추출하고 추출된 패턴을 멀티 레이어 퍼셉트론을 이용해 학습하여 메시지들을 분류한다. 이 시스템을 통해 약 93.5%의 필터링 정확도률을 얻었으며 키워드 선택 대신 스팸 메시지를 선택해 학습시킴으로서 사용하기 쉽고 사용자에 맞는 시스템을 구성할 수 있었다.

• Journal of Convergence for Information Technology
• /
• v.9 no.2
• /
• pp.7-16
• /
• 2019

The purpose of this study is to identify the characteristics of researches that have been conducted for the last 20 years through analyzing global research trends and evolutions of design articles from 1999 to 2018 with keyword network analysis. For this purpose, we selected 3,569 articles in 22 journals related to design research retrieved from the Scopus database and constructed keyword network model through the author keyword and index keyword. The frequency of the author and index keyword, the centrality of betweenness and degree were analyzed with the keyword network. The results show that design has been applied to various fields for recent 20 years, and the research trends of design could be quantitatively characterized by keyword network analysis. The result of this study could be used to suggest future research topics in the field of design based on quantitative and empirical data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.9
• /
• pp.68-73
• /
• 2016

This study was conducted to identify trends and knowledge structures associated with recent trends in Korean tourism from 2010 to 2015 using keyword data. To accomplish this, we constructed a network using keywords extracted from KCI journals. We then made a matrix describing the relationships between rows as papers and columns as keywords. A keyword network showed the connectivity of papers that have included one or more of the same keywords. Major keywords were then extracted using the cosine similarity between co-occurring keywords and components were analyzed to understand research trends and knowledge structure. The results revealed that subjects of tourism research have changed rapidly and variously. A few topics related to 'organization-employee' were major trends for several years, but intrinsic and extrinsic factors have been further subdivided and employees of specific fields have been targeted as subjects of research. Component analysis is useful for analyzing concrete research topics and the relationships between them. The results of this study will be useful for researchers attempting to identify new topics.

• Annual Conference on Human and Language Technology
• /
• 2022.10a
• /
• pp.3-9
• /
• 2022

본연구는 한국어 언어모델 트레이닝 단계에서 자주 사용되는 Fill-Mask 태스크와 직업 관련 키워드로 구성되는 각종 성별 유추 템플릿을 이용해 한국어 언어모델에서 발생하는 성별 편향 현상을 정량적으로 검증하고 해석한다. 결과를 봤을 때 현재 직업 키워드에서 드러나는 성별 편향은 각종 한국어 언어모델에서 이미 학습된 상태이며 이를 해소하거나 차단하는 방법을 마련하는 것이 시급한 과제이다.

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

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.468-475
• /
• 2022

The purpose of this study is to identify trends in domestic breaking research to derive characteristics and implications, and further suggest future research directions. To this end, literature analysis (the timing of paper publication, research method, research topic) and keyword analysis of 50 papers related to breaking published in academic journals registered with the Korea Research Foundation (KCI) were conducted. The research results are as follows. First, the trend by thesis publication period was first published in 2006, showed a slight increase in 2012, and then increased rapidly in 2021. Second, domestic braking-related research has been mainly focused on qualitative research (60%). Third, looking at the research topic, it is divided into three categories: identity establishment, culture and arts field, and sports field, of which studies related to identity establishment accounted for more than 60%. Finally, looking at the keywords frequently used in breaking papers, the most frequently appeared word was 'hip-hop', followed by 'culture'. Based on these results, implications were drawn to establishing the identity of braking through academic and theoretical approaches, practical approaches through the development of standardized textbooks and curriculum, strengthening the characteristics and capabilities of the field through integrated approaches, and changing to sports.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.421-431
• /
• 2018

The purpose of this study is to analyze the trends of 'traditional' terminology in Korea. We focus on an empirical investigation of how media reports are conveying 'tradition' terminology in our society by applying text mining and social network analysis techniques. The analysis covered 2,481,143 news articles related to 'tradition' terminology that appeared in the media since the 1920's. In this research, frequency analysis, association analysis and social network analysis were used on articles related to 'tradition' terminology from 1920 to 2017 by decade. By applying these data science techniques, we can grasp the meaning of social culture phenomenon related 'tradition' with objective and value-neutral position and understand the social symbolism which contains the tradition of the times.

• The Korean Journal of Applied Statistics
• /
• v.34 no.2
• /
• pp.267-278
• /
• 2021

As time goes by, the amount of data is increasing regardless of government, business, domestic or overseas. Accordingly, research on big data is increasing in academia. Statistics is one of the major disciplines of big data research, and it will be interesting to understand the research trend of statistics through big data in the growing number of papers in statistics. In this study, we analyzed what studies are being conducted through abstract data of statistical papers in Korea and abroad. Research trends in domestic and international were analyzed through the frequency of keyword data of the papers, and the relationship between the keywords was visualized through the Word Embedding method. In addition to the keywords selected by the authors, words that are importantly used in statistical papers selected through Textrank were also visualized. Lastly, 10 topics were investigated by applying the LDA technique to the abstract data. Through the analysis of each topic, we investigated which research topics are frequently studied and which words are used importantly.