• Annual Conference of KIPS
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• 2024.05a
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• pp.680-682
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• 2024

현재까지 연구되었던 뉴스 추천 시스템은 일반적으로 뉴스 제목, 뉴스 본문, 카테고리 정보 등의 텍스트 정보를 기반으로 사용자에게 맞춤 뉴스를 추천해주는 방식으로 동작한다. 구체적으로는 뉴스의 텍스트 정보를 통해 뉴스를 표현하는 임베딩 벡터를 생성하여 사용자 맞춤 뉴스를 추천하는 task-specific 한 아키텍처를 기반으로 동작한다. 기존 연구에서는 task-specific 아키텍처 내의 뉴스의 임베딩 벡터를 생성하는 과정에서 BERT 와 같은 언어모델을 이용하여 텍스트 정보를 더 잘 반영하고자 했다. 본 연구에서는 기존의 구조와 다르게, 뉴스 제목 인덱싱을 통해 전체 뉴스 추천 시스템에서의 언어모델을 충분히 활용할 수 있는 방식을 제안하고자 한다.

• Proceedings of the Korean Society for Information Management Conference
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• 1996.08a
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• pp.101-104
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• 1996

학문은 다양한 이론적 배경을 토대로 시대적 환경에 적응하여 발전한다. 즉, 서로의 영역을 공유하면서 새로운 이론을 창출하고 실제로 이를 응용하고 있는 것이다. 계량서지학, 계량과학학, 문헌과학학등으로 일컫고 있는 계량정보학은 문헌의 분석을 위해 수량학적 방법으로 적용하여 연구하는 학문으로, 활발히 연구되어 응용되고있는 분야는 텍스트검색시스템, OPACs, 비디오텍스시스템, 하이퍼텍스트시스템, CD-ROM, 온라인 정보서비스, 전자출판, 전자우편, 케이블 TV 등의 전자정보서비스 분야이다. 본 연구에서는 계량정보학의 사적변천과 연구영역, 그 응용과 실제를 고찰하였다.

• Annual Conference of KIPS
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• 2024.10a
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• pp.2-4
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• 2024

WordNet, Freebase, Wikidata와 같은 대규모 지식베이스에 대한 딥러닝 연구가 활발히 진행되고 있으나 이에 관한 온톨로지 매칭 연구는 거의 연구가 미비한 상태이다. 본 연구에서는 효율적인 온톨로지 매칭 방법을 개발하기 위해 데이터 수집 및 전처리, 텍스트 유사도 계산, 그리고 텍스트 유사도에 따른 결과 매칭의 세 가지 순차적 단계로 구성된 하나의 새로운 방법을 제안한다. 성능평가는 정보검색 분야에서 널리 활용되고 있는 재현율, 정밀도, F-측정값을 사용하였는데 제안한 방법이 기존의 모든 방법들보다 성능이 우수함을 보였다.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.201-209
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• 2020

The purpose of this study is to investigate the effect of narrative and expository texts on incidental vocabulary learning of Korean university EFL learners while reading. The experimental groups were divided into 3 groups. The first experimental group were exposed to narrative texts and second group received expository texts. And the third group were administered narrative and expository texts alternately. The vocabulary tests were conducted after the last session to assess the incidental vocabulary gains of the learners. The results indicated the superiority of the expository texts over narratives in terms of enhancing learners' incidental acquisition of unknown words. Moreover, the results showed that the blended reading group of expository and narrative texts did better on the vocabulary gains than those of narrative reading group and expository reading group. However, in the essay writing assessment, the expository group committed the most vocabulary errors in writing.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.171-179
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• 2020

Media technologies that have made the hyper-connected age change the way people use communication text. Ads texts actively used by companies are storytelling and storydoing. These two advertising texts are very effective in inducing people's emotions and forming participatory behavior. People's thinking tendency also influence persuasion. The results of this study are as follows according to the type of ads text and the thinking tendency of consumers. Consumers' attitudes toward ads turned out to be more positive in synthetic thinking. In analytical thinking, the storytelling ads texts induced more favorable responses. On the other hand, in comprehensive thinking, the story doing text was effective. The same result was found in the perception of premium value, willingness to pay premium price, and repurchase intention.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.596-603
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• 2015

A concept network is an essential knowledge base for semantic search engines, personalized search systems, recommendation systems, and text mining. Recently, studies of extending concept representation using external ontology have been frequently conducted. We thus propose a new way of building 3-dimensional text model-based concept networks using the world knowledge-level Wikipedia ontology. In fact, it is desirable that 'concepts' derived from text documents are defined according to the theoretical framework of formal concept analysis, since relationships among concepts generally change over time. In this paper, concept networks hidden in a given document collection are extracted more reasonably by representing a concept as a term-by-document matrix.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.523-530
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• 2024

Online, such as web pages and digital documents, have the ability to search for specific words or specific phrases that users want to search in real time. Printed materials such as printed books and reference books often have difficulty finding specific words or specific phrases in real time. This paper describes the development of a deep learning model for detecting text and a real-time character detection system using OCR for recognizing text. This study proposes a method of detecting text using the EAST model, a method of recognizing the detected text using EasyOCR, and a method of expressing the recognized text as a bounding box by comparing a specific word or specific phrase that the user wants to search for. Through this system, users expect to find specific words or phrases they want to search in real time in print, such as books and reference books, and find necessary information easily and quickly.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.2
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• pp.289-307
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• 2016

Infectious diseases such as Ebola virus disease become a social issue and draw public attention to be a major topic on news or research. As a result, there have been a lot of studies on infectious diseases using text-mining techniques. However, there is no research on content analysis of two media channels that have distinct characteristics. Accordingly, in this study, we conduct topic analysis between news (representing a social perspective) and academic research paper (representing perspectives of bio-professionals). As text-mining techniques, topic modeling is applied to extract various topics according to the materials, and the word co-occurrence map based on selected bio entities is used to compare the perspectives of the materials specifically. For network analysis, topic map is built by using Gephi. Aforementioned approaches uncovered the difference of topics between two materials and the characteristics of the two materials. In terms of the word co-occurrence map, however, most of entities are shared in both materials. These results indicate that there are differences and commonalties between social and academic materials.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.37-48
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• 2020

This study aimed at understanding trends of living lab studies and deriving implications for directions of the studies by utilizing text mining. The study included network analysis and topic modelling based on keywords and abstracts from total 166 thesis published between 2011 and November 2019. Centrality analysis showed that living lab studies had been conducted focusing on keywords like innovation, society, technology, development, user and so on. From the topic modelling, 5 topics such as "regional innovation and user support", "social policy program of government", "smart city platform building", "technology innovation model of company" and "participation in system transformation" were extracted. Since the foundation of KNoLL in 2017, the diversification of living lab study subjects has been made. Quantitative analysis using text mining provides useful results for development of living lab studies.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.23-30
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• 2020

This study used the text mining method to analyze the research trend of the Journal of Creative Information Culture(JCIC) which is the journal of convergence. The existing research trend analysis method has a limitation in that the researcher's personality is reflected using the traditional content analysis method. In order to complement the limitations of existing research trend analysis, this study used topic modeling. The English abstract of the paper was analyzed from 2015 to 2019 of the JCIC. As a result, the word that appeared most in the JCIC was "education," and eight research topics were drawn. The derived subjects were analyzed by educational subject, educational evaluation, learner's competence, software education and maker culture, information education and computer education, future education, creativity, teaching and learning methods. This study is meaningful in that it analyzes the research trend of the JCIC using text mining.