Fuzzy clustering of lecture videos based on topic modeling
Fuzzy clustering of lecture videos based on topic modeling
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DOI：
10.1109/cbmi.2016.7500264
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Subhasree Basu;Yi Yu;Roger Zimmermann
中科院分区：
文献类型：
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作者：
Subhasree Basu;Yi Yu;Roger Zimmermann
讲座录像是电子学习模式的重要组成部分。这些在线视频讲座包含多媒体材料，旨在以更有效的方式解释复杂的概念。这些视频大多按主题分组。然而，科目之间往往有重叠，例如数学和统计学。因此，教育内容明智的，一些讲座视频可以属于一个以上的主题。当它们只被标记为一个主题时，搜索讲座内容的学生可能会错过其中的一些视频。为了解决这个问题，我们的目标是提供一个集群的这些讲座视频的基础上，他们的教育内容，而不是他们的标题，这样的讲座不会错过的基础上的主题标签。我们的新算法使用主题建模自动字幕生成的视频成绩单，以提取这些视频的内容。我们从维基百科中为每个聚类选择具有代表性的文本文档。然后，我们计算从视频中提取的主题与聚类的代表性文档之间的相似度。最后，我们应用模糊聚类的基础上，这些相似性值，并提供了一个讲座内容为基础的聚类这些讲座视频。初步结果是合理的，并确认所提出的计划的有效性。
Lecture videos constitute an important part of the e-learning paradigm. These online video-lectures contain multimedia materials aimed at explaining complex concepts in a more effective way. The videos are mostly grouped by their subjects. However, often there are overlaps between the subjects, e.g. Mathematics and Statistics. Hence, educational content-wise, some of the lecture videos can belong to more than one subject. When they are labeled by only one subject, students searching for the content of the lecture might miss some of these videos. To solve this problem, we aim to provide a clustering of these lecture videos based on their educational content rather than their titles so that such lectures will not be missed out based on the subject labels. Our novel algorithm uses topic modeling on video transcripts generated by automatic captions to extract the contents of these videos. We choose representative text documents for each of the clusters from the Wikipedia. Then we calculate a similarity between the topics extracted from the videos and those of the representative documents of the clusters. Finally we apply fuzzy clustering based on these similarity values and provide a lecture-content based clustering for these lecture videos. The initial results are plausible and confirm the effectiveness of the proposed scheme.