Objective keyword selection for lecture video annotation

Abstract

This paper presents an objective keyword selection method called visualness with Lesk disambiguation (VLD) for describing educational videos with semantic tags. It extends the work on automatically extracting and associating meaningful keywords carried out in 'semantic tags for lecture videos' for efficient indexing and retrieval. VLD uses lecture videos and surrogates documents such as lecture transcripts to extract potential candidate keywords. The candidate keywords undergo a series of selection process extracting fewer but more meaningful keywords based on word sense disambiguation (WSD) and visual similarity. The objective metric then selects top ranking keywords by employing a rank cut-off method. The proposed metric is validated by comparing the automatically selected keywords to those obtained manually, suggesting that the words selected by the proposed objective metric correlate highly with those selected by viewers. The results are further compared to traditional term frequency inverse document frequency (TF-IDF) and state-of-the-art latent Dirichlet allocation (LDA) method, with an improved accuracy of 68.18% on 30 lecture videos.

Single document keywords extraction in Bahasa Indonesia using phrase chunking

“…Another method ranks candidate keywords to determine keywords from a single document based on the statistical features of the document content. For example, Imran et al [14] proposed a linguistic-based model called visualness with lesk disambiguation (VLD) to gather candidate keywords followed by word sense disambiguation and visual similarity to determine the important keywords. Combining statistical and linguistics approaches, the top candidate of keywords are acquired by measuring how well the candidates describe the document title.…”