• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.242-248
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• 2022

The article presents the analysis of modern methods of automatic comparison of original and unoriginal text to detect textual plagiarism. The study covers two types of plagiarism - literal, when plagiarists directly make exact copying of the text without changing anything, and intelligent, using more sophisticated techniques, which are harder to detect due to the text manipulation, like words and signs replacement. Standard techniques related to extrinsic detection are string-based, vector space and semantic-based. The first, most common and most successful target models for detecting literal plagiarism - N-gram and Vector Space are analyzed, and their advantages and disadvantages are evaluated. The most effective target models that allow detecting intelligent plagiarism, particularly identifying paraphrases by measuring the semantic similarity of short components of the text, are investigated. Models using neural network architecture and based on natural language sentence matching approaches such as Densely Interactive Inference Network (DIIN), Bilateral Multi-Perspective Matching (BiMPM) and Bidirectional Encoder Representations from Transformers (BERT) and its family of models are considered. The progress in improving plagiarism detection systems, techniques and related models is summarized. Relevant and urgent problems that remain unresolved in detecting intelligent plagiarism - effective recognition of unoriginal ideas and qualitatively paraphrased text - are outlined.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.246-254
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• 2019

The purpose of this study is to analyze the issues related to urban regeneration project at the present time through surveys and interviews of participants in the urban regeneration leading project of Suncheon city. Most of the comments were related to business fragmentation and things that should be improved in the future. The text network technique is applied to the subject analysis using unstructured text data. As a result of the frequency of appearance and analysis of page rank centrality between words, words of 'parking', 'need', 'lack', 'region' and 'resident' appeared at the top, and the result of analyzing the mediation centrality of key words showed 'culture', 'Need', 'region', 'inflow' and 'lack' appeared at the top. In the network analysis, the most central words appeared, and many words appeared in the important position in the sentence. Text network analysis has provided timely results in terms of sustainability after completion of the Suncheon City Regeneration Leading Project..

• 한국컴퓨터정보학회논문지
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• 제24권7호
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• pp.125-133
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• 2019

Because military weapons systems for special purposes are small and complex, they are not easy to maintain. Therefore, it is very important to maintain combat strength through quick maintenance in the event of a breakdown. In particular, naval ships are complex weapon systems equipped with various equipment, so other equipment must be considered for maintenance in the event of equipment failure, so that skilled maintenance personnel have a great influence on rapid maintenance. Therefore, in this paper, we analyzed maintenance data of defense equipment maintenance information system through text mining and social network analysis(SNA), and tried to identify the naval ship maintenance expertise. The defense equipment maintenance information system is a system that manages military equipment efficiently. In this study, the data(2,538cases) of some naval ship maintenance teams were analyzed. In detail, we examined the contents of main maintenance and maintenance personnel through text mining(word cloud, word network). Next, social network analysis(collaboration analysis, centrality analysis) was used to confirm the collaboration relationship between maintenance personnel and maintenance expertise. Finally, we compare the results of text mining and social network analysis(SNA) to find out appropriate methods for finding and finding naval ship maintenance expertise.

• 간호행정학회지
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• 제22권1호
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• pp.80-90
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• 2016

Purpose: The purpose of this study was to analyze students experiences during clinical practice in nursing management. Methods: Assessing through computerized databases, self-reflection reports of 57 students were analyzed. Text network analysis was applied to examine the research. The keywords from each student's reports were extracted by using the programs, KrKwic and NetMiner. Results: The results of the keyword network analysis of what students learned in the nursing process included 27 words. The keyword network analysis of what students learned from the problem solving process included 23 words and the keyword network analysis of improvements in Clinical Practice of Nursing included 31 words. Conclusion: Studies related to clinical practice have been increasing, and themes of the studies have also become broader. Further research is required to investigate factors affecting clinical practice specifically in nursing management. Further comparative studies are necessary to define differences in clinical practice systems related to improving nursing students competency.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• 정보관리학회지
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• 제31권4호
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• pp.49-68
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• 2014

본 연구의 목적은 국내 학술논문 데이터베이스에서 검색한 언어 네트워크 분석 관련 53편의 국내 학술논문들을 대상으로 하는 내용분석을 통해, 언어 네트워크 분석 방법의 기초적인 체계를 파악하기 위한 것이다. 내용분석의 범주는 분석대상의 언어 텍스트 유형, 키워드 선정 방법, 동시출현관계의 파악 방법, 네트워크의 구성 방법, 네트워크 분석도구와 분석지표의 유형이다. 분석결과로 나타난 주요 특성은 다음과 같다. 첫째, 학술논문과 인터뷰 자료를 분석대상의 언어 텍스트로 많이 사용하고 있다. 둘째, 키워드는 주로 텍스트의 본문에서 추출한 단어의 출현빈도를 사용하여 선정하고 있다. 셋째, 키워드 간 관계의 파악은 거의 동시출현빈도를 사용하고 있다. 넷째, 언어 네트워크는 단수의 네트워크보다 복수의 네트워크를 구성하고 있다. 다섯째, 네트워크 분석을 위해 NetMiner, UCINET/NetDraw, NodeXL, Pajek 등을 사용하고 있다. 여섯째, 밀도, 중심성, 하위 네트워크 등 다양한 분석지표들을 사용하고 있다. 이러한 특성들은 언어 네트워크 분석 방법의 기초적인 체계를 구성하는 데 활용할 수 있을 것이다.

• 융합정보논문지
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• 제9권3호
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• pp.68-74
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• 2019

본 연구는 최근 10년 동안(2009-2018) 국내 학술지에 발표된 감정노동(emotional labor) 관련 892편의 논문을 텍스트 마이닝(text-mining) 및 네트워크 분석(network analysis)을 활용하여 연구동향을 파악하는 것이 목적이다. 이를 위해 이들 논문의 주제어를 수집 및 코딩하여 최종적으로 871개의 노드(node)와 2625개의 링크(link)로 변환시켜 네트워크 텍스트로 분석하였다. 첫째, 네트워크 텍스트 분석 결과로 동시출현빈도에 따른 상위 4개 주요 주제어는 번아웃, 이직의도, 직무스트레스, 직무만족 순으로 나타났으며, 연결중심성에 따른 상위 4개 주제어들의 빈도와 연결중심성 모두 비교적 높은 것으로 확인되었다. 둘째, 연결중심성 상위 4개의 주제어를 바탕으로 자아(ego)연결망 분석을 실시하여 각 네트워크의 연결중심도에 대한 주제어를 제시하였다.

• 정보과학회 논문지
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• 제44권2호
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• pp.179-185
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• 2017

문서의 요약은 요약문 내의 문장들끼리 서로 연관성 있게 이어져야 하고 하나의 짜임새 있는 글이 되어야 한다. 본 논문에서는 위의 목적을 달성하기 위해 문장 간의 유사도와 수반 관계(Entailment)를 고려하여 문서 내에서 연관성이 크고 의미, 개념적인 연결성이 높은 문장들을 추출할 수 있도록 하였다. 본 논문에서는 Recurrent Neural Network 기반의 문장 관계 추론 모델과 그래프 기반의 랭킹(Graph-based ranking) 알고리즘을 혼합하여 단일 문서 추출요약 작업에 적용한 새로운 알고리즘인 TextRank-NLI를 제안한다. 새로운 알고리즘의 성능을 평가하기 위해 기존의 문서요약 알고리즘인 TextRank와 동일한 데이터 셋을 사용하여 성능을 비교 분석하였으며 기존의 알고리즘보다 약 2.3% 더 나은 성능을 보이는 것을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.319-323
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• 2022

Handwritten text recognition is one of the active research areas nowadays. The progress in this field differs in every language. For example, the progress in Arabic handwritten text recognition is still insignificant and needs more attentions and efforts. One of the most important fields in this is Arabic handwritten manuscript text recognition which focuses in extracting text from historical manuscripts. For eons, ancients used manuscripts to write everything. Nowadays, there are millions of manuscripts all around the world. There are two main challenges in dealing with these manuscripts. The first one is that they are at the risk of damage since they are written in primitive materials, the second challenge is due to the difference in writing styles, hence most people are unable to read these manuscripts easily. Therefore, we discuss in this study different papers that are related to this important research field.

• 대한한의학원전학회지
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• 제31권4호
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• pp.27-46
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• 2018

Objectives : In this paper, we investigated the applicability of text mining to Korean Medical Classics and suggest that researchers of Medical Classics utilize this methodology. Methods : We applied text mining to the Huangdi's internal classic, a seminal text of Korean Medicine, and visualized networks which represent connectivity of terms and documents based on vector similarity. Then we compared this outcome to the prior knowledge generated through conventional qualitative analysis and examined whether our methodology could accurately reflect the keyword of documents, clusters of terms, and relationships between documents. Results : In the term network, we confirmed that Qi played a key role in the term network and that the theory development based on relativity between Yin and Yang was reflected. In the document network, Suwen and Lingshu are quite distinct from each other due to their differences in description form and topic. Also, Suwen showed high similarity between adjacent chapters. Conclusions : This study revealed that text mining method could yield a significant discovery which corresponds to prior knowledge about Huangdi's internal classic. Text mining can be used in a variety of research fields covering medical classics, literatures, and medical records. In addition, visualization tools can also be utilized for educational purposes.