Predicting User Tags Using Semantic Expansion

  • Krishna Chandramouli
  • Tomas Piatrik
  • Ebroul Izquierdo
Part of the Communications in Computer and Information Science book series (CCIS, volume 255)

Abstract

Manually annotating content such as Internet videos, is an intellectually expensive and time consuming process. Furthermore, keywords and community-provided tags lack consistency and present numerous irregularities. Addressing the challenge of simplifying and improving the process of tagging online videos, which is potentially not bounded to any particular domain, we present an algorithm for predicting user-tags from the associated textual metadata in this paper. Our approach is centred around extracting named entities exploiting complementary textual resources such as Wikipedia and Wordnet. More specifically to facilitate the extraction of semantically meaningful tags from a largely unstructured textual corpus we developed a natural language processing framework based on GATE architecture. Extending the functionalities of the in-built GATE named entities, the framework integrates a bag-of-articles algorithm for effectively searching through the Wikipedia articles for extracting relevant articles. The proposed framework has been evaluated against MediaEval 2010 Wild Wild Web dataset, which consists of large collection of Internet videos.

Keywords

tag prediction video indexing user-contributed metadata speech recognition evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Fourteenth International Conference on Comput. Linguistics, pp. 539–545 (1992)Google Scholar
  2. 2.
    Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press (1999)Google Scholar
  3. 3.
    Bast H., Dupret G., Majumdar D., Piwowarski B.: Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis. Semantic Web Mining (2006)Google Scholar
  4. 4.
    Cimiano, P., Völker, J.: Text2onto - A Framework for Ontology Learning and Data-Driven Change Discovery. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Nemeth, Y., Shapira, B., Taeib-Maimon, M.: Evaluation of the real and perceived value of automatic and interactive query expansion. In: SIGIR (2006)Google Scholar
  6. 6.
    Shapira B., Taieb-Maimon M., Nemeth Y.: Subjective and objective evaluation of interactive and automatic query expansion. Online Information Review (2005)Google Scholar
  7. 7.
    Gong, Z., Cheang, C.W., Hou, U.L.: Web Query Expansion by WordNet. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 166–175. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Snow, R., Jurafsky, D., Ng, A.: Learning syntactic patterns for automatic hypernym discovery. In: NIPS (2005)Google Scholar
  9. 9.
    Nemrava, J.: Refining search queries using WordNet glosses. In: EKAW (2006)Google Scholar
  10. 10.
    Kliegr, T., Chandramouli, K., Nemrava, J., Svatek, V., Izquierdo, E.: Combining Captions and Visual Analysis for Image Concept Classification. In: Proceedings of the 9h International Workshop on Multimedia Data Mining (2008)Google Scholar
  11. 11.
    Kliegr, T.: Entity Classification by Bag of Wikipedia Articles. In: Doctoral Consortium, CIKM (2010)Google Scholar
  12. 12.
    Cucerza, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Proc. of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2007)Google Scholar
  13. 13.
    Budanitsky A., Hirst G.: Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krishna Chandramouli
    • 1
  • Tomas Piatrik
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Multimedia and Vision Research Group, School of Electronic Engineering and Computer ScienceQueen Mary, University of LondonLondonUK

Personalised recommendations