http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Yanggon Kim (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Company Name Discrimination in Tweets using Topic Signatures Extracted from News Corpus
Hong, Beomseok,Kim, Yanggon,Lee, Sang Ho Korean Institute of Information Scientists and Eng 2016 Journal of Computing Science and Engineering Vol.10 No.4
It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.
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Company Name Discrimination in Tweets using Topic Signatures Extracted from News Corpus
Beomseok Hong,Yanggon Kim,Sang Ho Lee 한국정보과학회 2016 Journal of Computing Science and Engineering Vol.10 No.4
It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.
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Dynamic Seed Selection for Twitter Data Collection
Hyoenchoel Lee(이현철),Changhyun Byun(변창현),Yanggon Kim(김양곤),Sang Ho Lee(이상호) 한국정보과학회 2014 정보과학회논문지 : 데이타베이스 Vol.41 No.4
트위터와 같은 소셜 네트워크 분석은 인간의 행동을 이해하거나, 화제가 되는 주제를 탐지하거나, 영향력 있는 사람을 식별하거나, 커뮤니티나 그룹을 발견하는데 흥미로운 시각을 제공할 수 있다. 하지만 소셜 네트워크가 가지는 특성(즉 데이터가 방대하고, 정교하지 않으며 또한 동적인 특성) 으로 인하여 소셜 네트워크에서 주제와 연관이 있는 데이터를 수집하는 것은 어려운 일이다. 본 논문은 주어진 주제와 관련 있는 트윗을 효과적으로 수집하기 위하여 시드 노드를 동적으로 선택하는 알고리즘을 제안한다. 본 알고리즘은 사용자의 영향력을 측정하기 위하여 사용자 속성을 활용하며, 수집 프로세스 중에 시드 노드를 동적으로 할당한다. 우리는 제안한 알고리즘을 실제 트윗 데이터에 적용하였으며, 만족할 만한 성능결과를 얻었다. Analysis of social media such as Twitter can yield interesting perspectives to understanding human behavior, detecting hot issues, identifying influential people, or discovering a group and community. However, it is difficult to gather the data relevant to specific topics due to the main characteristics of social media data; data is large, noisy, and dynamic. This paper proposes a new algorithm that dynamically selects the seed nodes to efficiently collect tweets relevant to topics. The algorithm utilizes attributes of users to evaluate the user influence, and dynamically selects the seed nodes during the collection process. We evaluate the proposed algorithm with real tweet data, and get satisfactory performance results.
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DED방식의 적층가공을 통한 금형으로의 응용사례 및 효과
김우성,홍명표,김양곤,서창희,이종원,이성희,성지현,Kim, Woosung,Hong, Myungpyo,Kim, Yanggon,Suh, Chang Hee,Lee, Jongwon,Lee, Sunghee,Sung, Ji Hyun 대한용접접합학회 2014 대한용접·접합학회지 Vol.32 No.4
Laser aided Direct Metal Tooling(DMT) process is a kind of Additive Manufacturing processes (or 3D-Printing processes), which is developed for using various commercial steel powders such as P20, P21, SUS420, H13, D2 and other non-ferrous metal powders, aluminum alloys, titanium alloys, copper alloys and so on. The DMT process is a versatile process which can be applied to various fields like the mold industry, the medical industry, and the defense industry. Among of them, the application of DMT process to the mold industry is one of the most attractive and practical applications since the conformal cooling channel core of injection molds can be fabricated at the slightly expensive cost by using the hybrid fabrication method of DMT technology compared to the part fabricated with the machining technology. The main objectives of this study are to provide various characteristics of the parts made by DMT process compared to the same parts machined from bulk materials and prove the performance of the injection mold equipped with the conformal cooling channel core which is fabricated by the hybrid method of DMT process.
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