Semantically Enhanced Term Frequency

  • Christof Müller
  • Iryna Gurevych
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)


In this paper, we complement the term frequency, which is used in many bag-of-words based information retrieval models, with information about the semantic relatedness of query and document terms. Our experiments show that when employed in the standard probabilistic retrieval model BM25, the additional semantic information significantly outperforms the standard term frequency, and also improves the effectiveness when additional query expansion is applied. We further analyze the impact of different lexical semantic resources on the IR effectiveness.


Information Retrieval Semantic Relatedness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tao, T., Wang, X., Mei, Q., Zhai, C.: Language Model Information Retrieval with Document Expansion. In: Proc. of HLT-NAACL 2006 (2006)Google Scholar
  2. 2.
    Yi, X., Allan, J.: A Comparative Study of Utilizing Topic Models for Information Retrieval. In: Proc. of ECIR 2009 (2009)Google Scholar
  3. 3.
    Sparck Jones, K., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: development and comparative experiments. Information Processing and Management 36(6) (2000)Google Scholar
  4. 4.
    Gabrilovich, E., Markovitch, S.: Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. In: Proc. of IJCAI 2007 (2007)Google Scholar
  5. 5.
    Egozi, O., Gabrilovich, E., Markovitch, S.: Concept-Based Feature Generation and Selection for Information Retrieval. In: Proc. of AAAI 2008 (2008)Google Scholar
  6. 6.
    Müller, C., Gurevych, I.: Using Wikipedia and Wiktionary in Domain-Specific Information Retrieval. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Peñas, A., Petras, V. (eds.) Evaluating Systems for Multilingual and Multimodal Information Access. LNCS, vol. 5706, pp. 219–226. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Zesch, T., Müller, C., Gurevych, I.: Using Wiktionary for Computing Semantic Relatedness. In: Proc. of AAAI 2008 (2008)Google Scholar
  8. 8.
    Amati, G.: Probability Models for Information Retrieval based on Divergence from Randomness. PhD thesis, Dept. of Computing Science, Univ. of Glasgow (2003)Google Scholar
  9. 9.
    Anderka, M., Stein, B.: The ESA Retrieval Model Revisited. In: Proc. of SIGIR 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christof Müller
    • 1
  • Iryna Gurevych
    • 1
  1. 1.Ubiquitous Knowledge Processing Lab, Computer Science DepartmentTechnische Universität DarmstadtGermany

Personalised recommendations