Video, a rich information source, is commonly used for capturing and sharing knowledge inlearning systems. However, the unstructured and linear features of video introduce difficultiesfor end users in accessing the knowledge captured in videos. To extract the knowledge structureshidden in a lengthy, multi-topic lecture video and thus make it easily accessible, we need to firstsegment the video into shorter clips by topic. Because of the high cost of manual segmentation,automated segmentation is highly desired. However, current automated video segmentationmethods mainly rely on scene and shot change detection, which are not suitable for lecturevideos with few scene/shot changes and unclear topic boundaries. In this article we investigatea new video segmentation approach with high performance on this special type of video:lecture videos. This approach uses natural language processing techniques such as nounphrases extraction, and utilizes lexical knowledge sources such as WordNet. Multiple linguisticbasedsegmentation features are used, including content-based features such as noun phrasesand discourse-based features such as cue phrases. Our evaluation results indicate that thenoun phrases feature is salient.
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