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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/100105

    Title: Adaptive Music Video Recommendation System Framework for Foreign English Language Learning
    Authors: Kuo, Chin-Hwa;Chi, Chen-Chung
    Contributors: 淡江大學資訊工程學系
    Keywords: Language Learning;Mutual Information;Document Difficulty;Document Readability;Music Recommender System
    Date: 2013-12-01
    Issue Date: 2015-01-28 11:07:37 (UTC+8)
    Publisher: 新北市:淡江大學
    Abstract: For many EFL learners, reading English articles or listening to music has been a good way to improve their English proficiency. However, it's not always easy to find the appropriate learning multimedia materials that can satisfy the learners' interest. The approach proposed in this paper uses a music video recommendation framework which fetches videos from Youtubeto be accessed either via mobile device or web browser. Learners can practice the usage of Verb-Noun collocations by watching the dynamic display of lyrics sentence by sentence, as well as listen to a recommended music video playing at the same time. The proposed system fetches collocation words by calculating mutual information of corpus, choosing music lyrics in the range of appropriate reading difficulty by cluster music lyric based on a particular vocabulary difficulty estimation method, and then providing a practice tool for mobile language learning.
    Relation: Journal of Applied Science and Engineering 16(4), pp.405-414
    DOI: 10.6180/jase.2013.16.4.08
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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