• The Korean Journal of Nuclear Medicine
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• v.35 no.3
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• pp.142-151
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• 2001

Purpose: This study was performed to search the relatively specific brain regions related to the semantic processing of Korean and English words on the one hand and the regions common to both on the other. Materials and Methods: Regional cerebral blood flow associated with different semantic tasks was examined using $[^{15}O]H_2O$ positron omission tomography in 13 healthy volunteers. The tasks consisted of semantic tasks for Korean words, semantic tasks for English words and control tasks using simple pictures. The regions specific and common to each language were identified by the relevant subtraction analysis using statistical parametric mapping. Results: Common to the semantic processing of both words, the activation site was observed in the fusiform gyrus, particularly the left side. In addition, activation of the left inferior temporal gyrus was found only in the semantic processing of English words. The regions specific to Korean words were observed in multiple areas, including the right primary auditory cortex; whereas the regions specific to English words were limited to the right posterior visual area. Conclusion: Internal phonological process is engaged in performing the visual semantic task for Korean words of the high proficiency, whereas visual scanning plays an important role in performing the task for English words of the low proficiency.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.97-111
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• 2016

In the creative economy paradigm, motivation of the opportunity based startup is a continuous concern to policy-makers. Recently, bigdata anlalytics challenge traditional methods by providing efficient ways to identify social trend and hidden issues in the public sector. In this study the authors introduce a case study using social bigdata analytics for conducting policy analysis. A semantic network analysis was employed using textual data from social media including online news, blog, and private bulletin board which create buzz on the startup business. Results indicates that each media has been forming different discourses regarding government's policy on the startup business. Furthermore, semantic network structures from private bulletin board reveal unexpected social burden that hiders opening a startup, which has not been found in the traditional survey nor experts interview. Based on these results, the authors found the feasibility of using social bigdata analysis for policy-making. Methodological and practical implications are discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1180-1182
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• 2010

오늘날 소프트웨어를 대상으로 하는 악성코드로부터의 공격이 잦아지면서, 소프트웨어 개발 프로세스에서부터의 보안 취약성 점검이 중요시되고 있다. 본 논문에서는 소프트웨어 보안 취약점 분석 기법 중 하나인 정적 분석에 사용되는 도구들을 살펴보고 비교하여 그 구조 및 특성을 분석 파악한다. 그리하여 우리의 궁극적 목표인 향상된 성능의 새로운 정적 분석 툴 개발의 기반을 마련하고자 한다.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.935-945
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• 2021

The purpose of this study is to analyze the social perception of Arduino lecture using big data analysis method. For this purpose, data from January 2012 to May 2021 were collected using the Textom website as a keyword searched for 'arduino + lecture' in blogs, cafes, and news channels of NAVER website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by opening the Textom website, Ucinet 6, and Netdraw programs. As a result of text mining analysis such as frequency analysis, TF-IDF analysis, and degree centrality it was confirmed that 'education' and 'coding' were the top keywords. As a result of CONCOR analysis for semantic network analysis, four clusters can be identified: 'Arduino-related education', 'Physical computing-related lecture', 'Arduino special lecture', and 'GUI programming'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to Arduino lecture on the Internet. The results of this study will be used as data that provides meaningful implications for instructors preparing for Arduino lectures, researchers studying the subject, and policy makers who establish software education or coding education and related policies.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.502-508
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• 2008

With the increased use of the internet and the tremendous amount of data it transfers, it is more necessary to summarize documents. We propose a new method using the Non-negative Semantic Variable Matrix (NSVM) and the generic important of semantic features obtained by Non-negative Matrix Factorization (NMF) to extract the sentences for automatic generic summarization. The proposed method use non-negative constraints which is more similar to the human's cognition process. As a result, the proposed method selects more meaningful sentences for summarization than the unsupervised method used the Latent Semantic Analysis (LSA) or clustering methods. The experimental results show that the proposed method archives better performance than other methods.

• Journal of KIISE
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• v.44 no.1
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• pp.36-43
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• 2017

Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.

• Journal of the Korean Society for information Management
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• v.24 no.4
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• pp.223-238
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• 2007

This paper contains an analysis of the methods that have been used to achieve or improve interoperability among metadata and discuss the possibilities and limits of semantic interoperability among metadata using crosswalk. After that a semantic metadata mapping process which is able to maximize the interoperability among metadata is suggested. The methodology consists of four steps such as identifying metadata schema, finding common data element concepts(DECs), grouping attributes by the DECs, and mapping into a table. An experimental application of the process was performed onto two human resource information metadata standards developed in Korea.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.1
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• pp.91-103
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• 2014

In this study, 70 national brands among men's wear brands were selected to examine linguistic characteristics of domestic national men's wear brand names. Linguistic factors which were used in national men's wear brand names were analyzed to understand their characteristics. Formative and semantic characteristics of each brand name were analyzed on the basis of the results from previous studies. It was found that long words with over four syllables are preferred than short words and single words in the form of noun are frequently used for domestic national men's wear brand names in terms of linguistic formality. English is most widely used in brand names, and European languages such as French, Spanish, and Italian are also used frequently under the influence of the country of origin. Next, the analysis result on the semantic characteristics of domestic national men's wear brand names showed that descriptive brand names are used to convey brand information directly and easily, or freestanding brand names which are absolutely irrelevant and newly coined words are chosen to create a characteristic image. In other words, brand names represent detailed business and product category of men's wear by forming a brand image of men's wear (ex. Man, Homme, Zio), and provide the information about properties and benefits related to the product such as dignity, masterpiece, and luxurious lifestyle to consumers by presenting the concept of the brand.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1540-1549
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• 2004

In this paper, we propose an algorithm to detect semantic events in golf video using Hidden Markov Model. The purpose of this paper is to identify and classify the golf events to facilitate highlight-based video indexing and summarization. In this paper we first define 4 semantic events, and then design HMM model with states made up of each event. We also use 10 multiple visual features based on MPEG-7 visual descriptors to acquire parameters of HMM for each event. Experimental results showed that the proposed algorithm provided reasonable detection performance for identifying a variety of golf events.