OffVid: A System for Linking Off-Topic Concepts to Topically Relevant Video Lecture Segments

Sharmila Reddy Nangi, Yashasvi Kanchugantla, Pavan Gopal Rayapati, Plaban Kumar Bhowmik

Abstract: We present a system for automatically connecting off-topic concepts from a video lecture to appropriate and topically relevant video lecture segments. The linked video lectures are expected to provide more detailed account of the corresponding off-topic concept. The system is realized with three modules: off-topic identification, topic base generation and segment linking. We modelled the problem of finding off-topic concept identification task as a community structure analysis in concept similarity graph. Word embedding-based technique has been used to generate topic specific video segments that act as the targets of off-topic concept connection candidates. The segmented videos are indexed using extracted topic by Solar search engine and are retrieved against queries formulated from off-topic concepts. The system has been evaluated on video lecture picked up from NPTEL MOOC platform. User study on the quality of recommendation has been found to be promising.