• Annual Conference on Human and Language Technology
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• pp.58-61
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• 2012

본 논문에서는 트위터 사용자들의 의견을 바이어스 탐지 하기 위해, 핵심 자질 추출 방법으로 HITS 그래프를 이용한 방법을 제안한다. 제안하는 핵심 자질 추출 방법은 사람이 직접 추출하지 못하는 자질도 추출할 수 있는 장점을 보였다. 제안한 핵심 자질 추출이 바이어스 탐지에 유효함을 검증하기 위해 4개의 토픽에 대해 평가 했을 때 제안 모델이 기존 모델보다 우수한 성능을 보였다.

• Korean Journal of Human Ecology
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• v.15 no.3
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• pp.417-423
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• 2006

본 고는 패션디자이너들이 함양해야 할 핵심 지적자질로서, 예술적, 사회적, 기술적 자질을 제시하고 있다. 이러한 핵심 지적자질은 "옷"이라는 디자인 오브제의 구상 및 추상적 본질을 둥의 조형적, 표현적, 그리고 상징적 특성을 문헌을 중심으로 이해하고 검토함으로써 제시되었다. 디자인 오브제로서 옷에 대한 본질에 관한 고찰은 패션디자이너의 역할을 정의하는 개념적 틀로서 사용되었으며, 디자이너의 핵심 지적자질을 함양하기 위한 구체적인 실천방안으로서, 문화를 분석, 체험하고 사회에서 파생되는 제반 인문, 사회, 과학, 기술관련 이슈들을 이해하고 습득하려는 능동적 실천을 권장하고 있다.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.69-74
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• 2012

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.341-350
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• 2021

This study focused on the orientation of makeup education, considering the characteristics of the makeup sector. For this, the academic categories and properties of makeup in the theoretical areas of makeup education were analyzed. The practical fields of makeup techniques were divided into five different areas, and their characteristics and core competencies were analyzed. Then, the study results were verified through an in-depth interview. The results found the following: The academic categories of makeup are correlated with various fields such as art, technique, design, public health, science, medicine, and humanities. Analysis on the five different fields was mostly matched in practices. However, core competencies revealed a big difference in stage performances and fashion shows. Overall, basic beauty makeup, basic stage makeup, and basic media makeup techniques were found in terms of the core competencies. In particular, there was a high demand for hairstyling skills.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.499-517
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• 2008

In this paper, we propose an effective emotion feature extraction method for Korean and evaluate their availability in sentiment classification. Korean emotion features are expanded from several representative emotion words and they play an important role in building in an effective sentiment classification system. Firstly, synonym information of English word thesaurus is used to extract effective emotion features and then the extracted English emotion features are translated into Korean. To evaluate the extracted Korean emotion features, we represent each document using the extracted features and classify it using SVM(Support Vector Machine). In experimental results, the sentiment classification system using the extracted Korean emotion features obtained more improved performance(14.1%) than the system using content-words based features which have generally used in common text classification systems.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.642-652
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• 2009

In order to improve the performance of the existing relation extraction approaches, we propose a method for combining two pivotal concepts which play an important role in classifying semantic relationships between entities in text. Having built a composite kernel-based relation extraction system, which incorporates both entity features and syntactic structured information of relation instances, we define nine classes of lexical features and synthetically apply them to the system. Evaluation on the ACE RDC corpus shows that our approach boosts the effectiveness of the existing composite kernels in relation extraction. It also confirms that by integrating the three important features (entity features, syntactic structures and contextual lexical features), we can improve the performance of a relation extraction process.

• Journal of the Korean Society for information Management
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• v.23 no.1
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• pp.279-299
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• 2006

This study utilizes various approaches of machine learning in the process of automatically assigning descriptors to journal articles. The effectiveness of feature selection and the size of training set were examined, after selecting core journals in the field of information science and organizing test collection from the articles of the past 11 years. Regarding feature selection, after reducing the feature set using $x^2$ statistics(CHI) and criteria that prefer high-frequency features(COS, GSS, JAC), the trained Support Vector Machines(SVM) performed the best. With respect to the size of the training set, it significantly influenced the performance of Support Vector Machines(SVM) and Voted Perceptron(VTP). However, it had little effect on Naive Bayes(NB).

• Proceedings of the Korean Information Science Society Conference
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• pp.898-900
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• 2004

시간에 중속적인 문서집합에서 사람이 만든 요약문은 시간에 따른 중요 내용의 분포를 보여준다. 본 논문은 다중 문서에 시간 자질을 이용한 문서의 분류와 시간별 문서집합에서 핵심문장과 부가문장을 선별하고, 문장간의 계층적인 클러스터링을 통해서 중요 문장을 선별하는 방법을 제안한다. 동일한 주제를 갖는 문서집합에서 사랑이 선택한 중요 문장에 대해서 제안한 방법은 50% 정확률을 나타냈다.

• Proceedings of the Korea Information Processing Society Conference
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• pp.395-398
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• 2011

트위터 사용자들은 어떠한 이슈에 대해 트위터를 통해 빠르고 간결하게 다른 사람들과의 지속적인 커뮤니케이션을 원하고, 이러한 특징은 이슈 별 사건에 따라 트윗 개수에 영향을 미치게 된다. 만약 어느 하나의 사회적 이슈에 대해 어떠한 사건이 일어나게 되면 그때의 트윗 개수는 폭발적으로 증가하게 된다. 본 논문에서는 이러한 특징을 이용하여 트위터 자료를 시간별로 분석하여 사건을 인식하고, 감성 자질과 카이제곱 값을 이용해 해당 날짜에 대한 핵심 사건을 추출한다.