• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.197-200
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• 2018

본 연구에서는 <네이버 영화> 페이지의 리뷰 데이터를 수집하여, 출현 빈도가 높은 단어를 중심으로 영화 관람객의 반응을 시각화하는 작업을 수행하였다. 이를 위해 총 6편의 영화를 선정하여 데이터 수집 및 정제과정을 거쳤으며, 의미연결망 분석(Semantic network analysis)을 활용하여 단어 간 관계성을 파악하고자 하였다. 데이터 시각화 작업에는 UCINET과 함께 패키지화된 NetDraw가 사용되었다. 본 연구의 시사점은 문장으로 작성된 영화 관람객의 리뷰를 키워드 중심으로 시각화하여, 소비자들의 반응을 한 눈에 확인하는 리뷰 인터페이스 구현이 가능한지 탐색하였다는 점이다.

• 대한건축학회논문집:계획계
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• 제35권7호
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• pp.101-108
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• 2019

In this research, I conducted a semantic network analysis centering on media articles on purchasing, revitalizing, and utilizing the former Busan branch of Hansung Bank, a modern architectural heritage. We sought the most efficient analysis elements for the analysis of the social arguments about preservation and utilization embedded in media articles. For this reason, Degree Centrality measures how many connections the word described in the media article has, and Betweenness Centrality measures the influence that controls the flow of information through correlation I examined. In addition, keyword that express the theme well examined the aggregation structure in each sub-network. In this research, in theoretical terms, it makes sense in that the social discussion embedded in the article of the mass media is grasped empirically through semantic network analysis of words. Methodological aspect is best when it includes nouns and adjectives and the distance between words is more than four words in the analysis of the cohesive structure of the semantic network to determine whether the influence of social discussions is best assessed through the connection between words to media articles.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.892-904
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• 2021

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.222-230
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• 2023

The purpose of this study is to provide basic data on sports entertainment programs by collecting data on unstructured data generated by Naver and Google for SBS entertainment program 'Women Who Score Goal', which began regular broadcast in June 2021, and analyzing public perceptions through data mining, semantic matrix, and CONCOR analysis. Data collection was conducted using Textom, and 27,911 cases of data accumulated for 16 months from June 16, 2021 to October 15, 2022. For the collected data, 80 key keywords related to 'Kick a Goal' were derived through simple frequency and TF-IDF analysis through data mining. Semantic network analysis was conducted to analyze the relationship between the top 80 keywords analyzed through this process. The centrality was derived through the UCINET 6.0 program using NetDraw of UCINET 6.0, understanding the characteristics of the network, and visualizing the connection relationship between keywords to express it clearly. CONCOR analysis was conducted to derive a cluster of words with similar characteristics based on the semantic network. As a result of the analysis, it was analyzed as a 'program' cluster related to the broadcast content of 'Kick a Goal' and a 'Soccer' cluster, a sports event of 'Kick a Goal'. In addition to the scenes about the game of the cast, it was analyzed as an 'Everyday Life' cluster about training and daily life, and a cluster about 'Broadcast Manipulation' that disappointed viewers with manipulation of the game content.

• 농촌계획
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• 제23권3호
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• pp.37-51
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• 2017

There has been a growing concern on ontology especially in recent knowledge-based industry and defining a field-customized semantic word network is essential for building it. In this paper, a word network for ontology is established with 785 publications of Korean Society of Rural Planning(KSRP), from 1995 to 2017. Semantic relationships between words in the publications were quantitatively measured with the 'normalized pointwise mutual information' based on the information theory. Appearance and co-appearance frequencies of nouns and adjectives in phrases are analyzed based on the assumption that a 'noun phrase' represents a single 'concept'. The word network of KSRP was compared with that of $WordNet^{TM}$, a world-wide thesaurus network, for the verification. It is proved that the KSRP's word network, established in this paper, provides words' semantic relationships based on the common concepts of Korean rural planning research field. With the results, it is expecting that the established word network can present more opportunity for preparation of the fourth industrial revolution to the field of the Korean rural planning.

• 한국치위생학회지
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• 제22권6호
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• pp.495-502
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• 2022

Objectives: The purpose of this study was to analyze research trends in dental hygiene using topic modeling and semantic network analysis. Methods: A total of 261 published studies were collected 686 key words from the Research Information Sharing Service (RISS) by 2019-2021. Topic modeling and semantic network analysis were performed using Textom. Results: The most frequently and frequency-inverse document frequently key words were 'dental hygienist', 'oral health', 'elderly', 'periodontal disease', 'dental hygiene'. N-gram of key words show that 'dental hygienist-emotional labor', 'dental hygienist-elderly', 'dental hygienist-job performance', 'oral health-quality of life', 'oral health-periodontal disease' etc. were frequently. Key words with high degree centrality were 'dental hygienist (0.317)', 'oral health (0.239)', 'elderly (0.127)', 'job satisfaction (0.057)', 'dental care (0.049)'. Extracted topics were 5 by topic modeling. Conclusions: Results from the current study could be available to know research trends in dental hygiene and it is necessary to improve more detailed and qualitative analysis in follow-up study.

• 한국정보통신학회논문지
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• 제24권11호
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• pp.1437-1444
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• 2020

컴퓨터 비전 시스템의 발달로 보안, 생체인식, 의료영상, 자율주행 등의 분야에 많은 발전이 있었다. 자율주행 분야에서는 특히 딥러닝을 이용한 객체인식, 탐지 기법이 주로 사용되는데, 자동차가 갈 수 있는 영역을 판단하기 위한 도로영역 인식이 특히 중요한 문제이다. 도로 영역은 일반적인 객체탐지에서 활용되는 사각영역인식과는 달리 비정형적인 형태를 띠므로, ROI 기반의 객체인식 구조는 적용할 수 없다. 본 논문에서는 Semantic segmentation 기법을 사용한 비정형적인 도로영역 인식에 맞는 심층 신경망 구조를 제안한다. 또한 도로영역에 특화된 네트워크 구조인 Multi-scale semantic segmentation 기법을 사용하여 성능이 개선됨을 입증하였다.

• 영재교육연구
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• 제23권6호
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• pp.881-898
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• 2013

본 연구의 목적은 야외지질학습에서 학습한 퇴적환경에 대한 언어네트워크가 과학영재와 일반학생에 따라 어떤 차이가 있는지 알아보기 위한 것이다. 이를 위하여 각각 15명의 과학영재와 일반학생을 연구 대상으로 선정하였으며, 퇴적환경의 학습장소로 적절한 채석강에서 야외 지질학습을 수행하였다. 자료 수집은 야외지질학습 관찰 및 학생들의 탐구활동 보고서를 통해 이루어졌으며, 이렇게 수집된 보고서는 언어네트워크분석을 이용하여 분석하였다. 분석 결과 첫째, 야외지질학습 동안 학습된 퇴적환경에 대한 언어네트워크는 과학영재들이 일반학생에 비해 더 크고 복잡한 구조를 이루고 있었다. 둘째, 과학영재는 퇴적물을 이루는 자갈의 특성을 분급, 원마도 등의 개념과 관련지어 퇴적환경을 적절히 해석하고 있었지만 일반학생들은 퇴적환경과 관련된 개념이 파편화되어 사용되고 있었다.