Empirical Analysis of Semantic Metadata Extraction from Video Lecture Subtitles

Video lectures improve the learning experiences considering individual's needs and learning styles. However, the large amount of educational content and their availability in a fragmented way turns difficult the tasks of accessing these resources and understanding the concepts under study. Extracting relevant information from video lectures can be useful for recommendation purposes and for helping the interpretation of a concept in an exact moment of a lecture. The extraction of semantic metadata from a video natural language subtitle involves challenges in dealing with informal aspects of language and the detection of semantic classes from the text. In this paper, we conduct an empirical analysis of semantic annotation approaches supported by ontologies in the extraction of relevant metadata from textual transcriptions of video lectures in Computer Science. The obtained results indicate that existing tools can be useful for the studied task and the video lecture semantic metadata extraction process is highly influenced by the underlying ontologies.