Richard Bache
Fabio Crestani
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.
- 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.Google Scholar
- R. Baeza-Yates, B Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, Harrow, England 1999. Google ScholarDigital Library
- F. Crestani, M. Lalmas, C. J. van Rijsbergen, I. Campbell, "Is this document relevant?..probably": a survey of probabilistic models in information retrieval", ACM Computing Surveys, Vol. 30, No. 4, pp. 528--552, 1998. Google ScholarDigital Library
- N. Fuhr, Models for Retrieval With Probabilistic Indexing, Information Processing & Management, Vol. 25, No. 1 pp. 55--72, 1989. Google ScholarDigital Library
- 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.Google Scholar
- S. Mizzaro, Relevance: The whole history. In T. Bellardo Hahn and M. Buckland, editors, Historical Studies in Information Science, pages 221--244. 1998.Google Scholar
- J. M. Ponte, W. B. Croft, "A Language Modeling Approach to Information Retrieval", in Proceedings of the Twenty First ACM-SIGIR, pp 275--281, Melbourne, Australia, 1998. Google ScholarDigital Library
- C. J. van Rijsbergen, Information Retrieval, Buttereworths, London, England, 1979. Google ScholarDigital Library
- S. E. Robertson, The Probability Ranking Principle in IR, in K. Spark-Jones, P. Willett (Eds), Readings In Information Retrieval, Morgan Kaufmann Publishers, San Francisco, California, 1997. Google ScholarDigital Library
- 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.Google Scholar
- K. Sparck Jones, S. Walker and S. E. Robertson, A probabilistic model of information retrieval: development and comparative experiments. Information Processing and Management 36, Part 1 779--808, 2000. Google ScholarDigital Library
- H. Turtle, W. B. Croft, Inference Networks for Document Retrieval, in K. Spark-Jones, P. Willett (Eds), Readings In Information Retrieval, Morgan Kaufmann Publishers, San Francisco, California, 1997. Google ScholarDigital Library
- E. Vorrhees, Overview of TREC 2003, http://trec.nist.gov/pubs/trec12/papers/OVERVIEW.12.pdf (last accessed 27/04/07).Google Scholar
Index Terms
- Language models, probability of relevance and relevance likelihood
Recommendations
Pseudo relevance feedback using semantic clustering in relevance language model
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementPseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By ...
Enhancing relevance models with adaptive passage retrieval
ECIR'08: Proceedings of the IR research, 30th European conference on Advances in information retrievalPassage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous ...
Time-based relevance models
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information RetrievalThis paper addresses blog feed retrieval where the goal is to retrieve the most relevant blog feeds for a given user query. Since the retrieval unit is a blog, as a collection of posts, performing relevance feedback techniques and selecting the most ...
Comments