Language models, probability of relevance and relevance likelihood

ABSTRACT

This paper proposes a measure of relevance likelihood derived specifically for language models. Such a measure may be used to guide a user on how far to browse through the list of retrieved items or for pseudo-relevance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will contain fragments of this ideal document. Thus, in deriving this measure we propose a novel way of capturing relevance in Language Modelling.

REFERENCES

• 1: Bache, R., Crestani, F., Canter, D., Youngs, D., Application of Language Models to Suspect Prioritisation and Suspect Likelihood in Serial Crimes, to appear at International Workshop on Computer Forensics, 2007.
• 2: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 3: Fabio Crestani , Mounia Lalmas , Cornelis J. Van Rijsbergen , Iain Campbell, “Is this document relevant?…probably”: a survey of probabilistic models in information retrieval, ACM Computing Surveys (CSUR), v.30 n.4, p.528-552, Dec. 1998 [doi>10.1145/299917.299920]
• 4: Norbert Fuhr, Models for retrieval with probabilistic indexing, Information Processing and Management: an International Journal, v.25 n.1, p.55-72, 1989 [doi>10.1016/0306-4573(89)90091-5]
• 5: J. Lafferty, C Zhai, Probabalistic Relevance Models Based on Document and Query Generation, in (ed. Croft, W. B. and Lafferty, J.), Language Modeling for Information Retrieval, Kluwer Academic Publishers, Dordrecht 2003.
• 6: S. Mizzaro, Relevance: The whole history. In T. Bellardo Hahn and M. Buckland, editors, Historical Studies in Information Science, pages 221--244. 1998.
• 7: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 8: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 9: S. E. Robertson, The probability ranking principle in IR, Readings in information retrieval, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1997
• 10: K. Spark-Jones, S. Robertson, D. Hiemstra, H. Zaragoza, Language Modelling and Relevance, in (ed. Croft W. B. and Lafferty J.), Language Modeling for Information Retrieval, Kluwer Academic Publishers, Dordrecht 2003.
• 11: K. Sparck Jones , S. Walker , S. E. Robertson, A probabilistic model of information retrieval: development and comparative experiments, Information Processing and Management: an International Journal, v.36 n.6, p.779-808, Nov.06.2000 [doi>10.1016/S0306-4573(00)00015-7]
• 12: Howard Turtle , W. Bruce Croft, Inference networks for document retrieval, Readings in information retrieval, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1997
• 13: E. Vorrhees, Overview of TREC 2003, http://trec.nist.gov/pubs/trec12/papers/OVERVIEW.12.pdf (last accessed 27/04/07).

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Measurement, Theory

Keywords:
language modelling, ranking function, relevance likelihood