Comparative study on subject classification of academic videos using noisy transcripts

Hau Wen Chang, Hung Sik Kim, Shuyang Li, Jeongkyu Lee, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

With the advance of Web technologies, the number of "academic" videos available on the Web (e.g., online lectures, web seminars, conference presentations, or tutorial videos) has increased explosively. A fundamental task of managing such videos is to classify them into relevant subjects. For this task, most of current content providers rely on keywords to perform the classification, while active techniques for automatic video classification focus on utilizing multi-modal features. However, in our settings, we argue that both approaches are not sufficient to solve the problem effectively. Keywords based method is very limited in terms of accuracy, while features based one lacks semantics to represent academic subjects. Toward this problem, in this paper, we propose to transform the video subject classification problem into the text categorization problem by exploiting the extracted transcripts of videos. Using both real and synthesized data, (1) we extensively study the validity of the proposed idea, (2) we analyze the performance of different text categorization methods, and (3) we study the impact of various factors of transcripts such as quality and length towards academic video classification problem.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010
Pages67-72
Number of pages6
DOIs
StatePublished - Dec 1 2010
Event4th IEEE International Conference on Semantic Computing, ICSC 2010 - Pittsburgh, PA, United States
Duration: Sep 22 2010Sep 24 2010

Publication series

NameProceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010

Other

Other4th IEEE International Conference on Semantic Computing, ICSC 2010
CountryUnited States
CityPittsburgh, PA
Period9/22/109/24/10

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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  • Cite this

    Chang, H. W., Kim, H. S., Li, S., Lee, J., & Lee, D. (2010). Comparative study on subject classification of academic videos using noisy transcripts. In Proceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010 (pp. 67-72). [5628857] (Proceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010). https://doi.org/10.1109/ICSC.2010.91