Semantic Hierarchical Indexing for Online Video Lessons Using Natural Language Processing

Abstract

Huge quantities of audio and video material are available at universities and teaching institutions, but their use can be limited because of the lack of intelligent search tools. This paper describes a possible way to set up an indexing scheme that offers a smart search modality, that combines semantic analysis of video/audio transcripts with the exact time positioning of uttered words. The proposal leverages NLP methods for topic modeling with lexical analysis of lessons’ transcripts and builds a semantic hierarchical index into the corpus of lessons analyzed. Moreover, using abstracting summarization, the system can offer short summaries on the subject semantically implied by the search carried out.

Keywords

• video lesson indexing
• semantic indexing
• topic modeling
• natural language processing
• abstractive summarization