Automated Question Answering From Lecture Videos: NLP vs. Pattern Matching

This paper explores the feasibility of automated question answering from lecture video materials used in conjunction with PowerPoint slides. Two popular approaches to question answering are discussed, each separately tested on the text extracted from videotaped lectures: 1) the approach based on Natural Language Processing (NLP) and 2) a self-learning probabilistic pattern matching approach. The results of the comparison and our qualitative observations are presented. The advantages and shortcomings of each approach are discussed in the context of video applications for e-learning or knowledge management.