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VideoCLEF 2008: ASR Classification with Wikipedia Categories

  • Jens Küsrsten
  • Daniel Richter
  • Maximilian Eibl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

This article describes our participation at the VideoCLEF track. We designed and implemented a prototype for the classification of the Video ASR data. Our approach was to regard the task as text classification problem. We used terms from Wikipedia categories as training data for our text classifiers. For the text classification the Naive-Bayes and kNN classifier from the WEKA toolkit were used. We submitted experiments for classification task 1 and 2. For the translation of the feeds to English (translation task) Google’s AJAX language API was used. Although our experiments achieved only low precision of 10 to 15 percent, we assume those results will be useful in a combined setting with the retrieval approach that was widely used. Interestingly, we could not improve the quality of the classification by using the provided metadata.

Keywords

Evaluation Experimentation Automatic Speech Transcripts Video Classification 

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References

  1. 1.
    Kürsten, J., Richter, D., Eibl, M.: VideoCLEF 2008: ASR Classification based on Wikipedia Categories. In: Working Notes for the CLEF 2008 Workshop, Aarhus, Denmark, September 17-19 (2008)Google Scholar
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    Larson, M., Newman, E., Jones, G.: Overview of VideoCLEF 2008: Automatic Generation of Topic-based Feeds for Dual Language Audio-Visual Content. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 906–917. Springer, Heidelberg (2009)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jens Küsrsten
    • 1
  • Daniel Richter
    • 1
  • Maximilian Eibl
    • 1
  1. 1.Faculty of Computer Science, Chair Computer Science and MediaChemnitz University of TechnologyChemnitzGermany

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