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  • -放送・映画部門での「韓流」受容の過程を中心として-
    鄭 榮蘭
    国際情報研究
    2014年 11 巻 1 号 44-55
    発行日: 2014/12/19
    公開日: 2014/12/19
    ジャーナル フリー
    The phrase “Korean Wave” appeared for the first time in a Chinese press. It was used in Youth News of Beijing in November, 1999, in a situation where Korean TV dramas spread to China, Taiwan, and Hong Kong.It was intended as a warning about young people getting absorbed in the Korean popular culture. The tide made its way to Japan, bringing about a “Korean boom” in 2003. Here in Japan, the expression “Korean Waves” covers not only Korean dramas, movies, music and other areas of popular culture in vogue here, but also remarkable events in politics, economy, international relations, news media and academic worlds in Korea. Talks about Korean softwares and contents promotion policies are referred to as part of Korean Waves. What have “Korean Waves” brought to Japan in the last decade of years? In order to see this, the author will examine the process in which the Japanese have brought in the Korean Culture, especially TV dramas and movies, and how antipathies and dislikes to Korean Culture have grown.
  • 柴田 雅博, 冨浦 洋一, 西口 友美
    人工知能学会論文誌
    2009年 24 巻 6 号 507-519
    発行日: 2009年
    公開日: 2009/09/04
    ジャーナル フリー
    We propose an open-ended dialog system that generates a proper sentence to a user's utterance using abundant documents on the World Wide Web as sources. Existing knowledge-based dialog systems give meaningful information to a user, but they are unsuitable for open-ended input. The system Eliza can handle open-ended input, but it gives no meaningful information. Our system lies between the above two dialog systems; it converses on various topics and gives meaningful information related to the user's utterances. The system selects an appropriate sentence as a response from documents gathered through the Web, on the basis of surface cohesion and shallow semantic coherence. The surface cohesion follows centering theory and the semantic coherence is calculated on the basis of the conditional distribution and inverse document frequency of content words (nouns, verbs, and adjectives.) We developed a trial system to converse about movies and experimentally found that the proposed method generated 66% appropriate responses.
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