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Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections

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Abstract

Term-weighting schemes are vital to the performance of Information Retrieval models that use term frequency characteristics to determine the relevance of a document. The vector space model is one such model in which the weights assigned to the document terms are of crucial importance to the accuracy of the retrieval system. This paper describes a genetic programming framework used to automatically determine term-weighting schemes that achieve a high average precision. These schemes are tested on standard test collections and are shown to perform as well as, and often better than, the modern BM25 weighting scheme. We present an analysis of the schemes evolved to explain the increase in performance. Furthermore, we show that the global (collection wide) part of the evolved weighting schemes also increases average precision over idf on larger TREC data. These global weighting schemes are shown to adhere to Luhn’s resolving power as middle frequency terms are assigned the highest weight. However, the complete weighting schemes evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections

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References

  • Cummins R., O’Riordan C. (2004a). Determining General Term Weighting Schemes for the Vector Space Model of Information Retrieval Using Genetic Programming. In 15th Artificial Intelligence and Cognitive Science Conference (AICS 2004). Galway-Mayo Institute of Technology, Castlebar Campus Ireland

  • Cummins R., O’Riordan C. (2004b). Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Modelof Information Retrieval. In: Keijzer M (eds). Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference. Seattle, Washington, USA

    Google Scholar 

  • Darwin C. (1859). The Origin of the Species by means of Natural Selection, or The Preservation of Favoured Races in the Struggle for Life. First edition

  • Fan W., Gordon M.D., Pathak P. (2004). A Generic Ranking Function Discovery Framework by Genetic Programming For Information Retrieval. Information Processing & Management

  • Goldberg D.E. (1989). Genetic Algorithms in Search, Optimisation and Machine learning. Addison-Wesley

  • Gordon M. (1988). Probabilistic and Genetic Algorithms in Document Retrieval. Communication of the ACM 31(10):1208–1218

    Article  Google Scholar 

  • Greiff W. (1998). A Theory of Term Weighting Based on Exploratory Data Analysis. In Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’98). Melbourne, Australia

  • Hersh W., Buckley C. Leone T.J., Hickam D. (1994). OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Springer-Verlag, New York Inc, pp. 192–201

    Google Scholar 

  • Horng J., Yeh C. (2000). Applying Genetic Algorithms to Query Optimization in Document Retrieval. Information Processing & Management 36(5):737–759

    Article  Google Scholar 

  • Kim S., Zhang B.T. (2001). Evolutionary Learning of Web-Document Structure for Information Retrieval. In Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001)

  • Koza J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge MA USA

    MATH  Google Scholar 

  • Kuscu I. (2000). Generalisation and Domain Specific Functions in Genetic Programming. In Proceedings of the 2000 Congress on Evolutionary Computation CEC00. 1393–1400, IEEE Press

  • Luhn H. (1958). The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 159–165

  • Oren N. (2002). Re-examining tf.idf Based Information Retrieval with Genetic Programming. Proceedings of SAICSIT

  • Porter M. (1980). An algorithm for Suffix Stripping. Program 14(3):130–137

    Google Scholar 

  • Robertson S.E., Sparck Jones K. (1976). Relevance Weighting of Search Terms. Journal of American Society for Information Sciences 27(3):129–146

    Article  Google Scholar 

  • Robertson S.E., Walker S. (1997). On Relevance Weights with Little Relevance Information. In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 16–24, ACM Press

  • Robertson S.E., Walker S., Hancock-Beaulieu M., Gull A., Lau M. (1995). Okapi at TREC-3. In Harman, D. K. (ed.) The Third Text REtrieval Conference (TREC-3) NIST

  • Salton G., Buckley C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management 24(5):513–523

    Article  Google Scholar 

  • Salton G., Wong A., Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11):613–620

    Article  MATH  Google Scholar 

  • Salton G., Yang C. S. (1973). On the Specification of Term Values in Automatic indexing. Journal of Documentation 29:351–372

    Article  Google Scholar 

  • Trotman A. (2004). An Artificial Intelligence Approach to Information Retrieval (Abstract Only). In SIGIR. p. 603

  • Van Rijsbergen C.J. (1979). Information Retrieval, 2nd ed. Department of Computer Science, University of Glasgow

  • Vrajitoru D. (1998). Crossover Improvement for the Genetic Algorithm in Information Retrieval. Information Processing and Management 34(4):405–415

    Article  Google Scholar 

  • Vrajitoru D. (2000). In Crestani, F. & Pasi, G. (eds.) Soft Computing in Information Retrieval. Techniques and Applications, 199–222. Physica-Verlag

  • Yang J., Korfhage R. (1993). Query Optimization in Information Retrieval Using Genetic Algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms. 603–611

  • Yang Y., Pedersen J.O. (1997). A Comparative Study on Feature Selection in text Categorization. In: Fisher D.H. (eds). Proceedings of ICML-97, 14th Inter- national Conference on Machine Learning. Nashville, US. Morgan Kaufmann Publishers, San Francisco, US, pp. 412–420

    Google Scholar 

  • Zipf G. (1949). Human Behaviour and the Principle of Least Effort. Addison-Wesley, Cambridge, MA

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, National University of Ireland, Galway, Ireland

    Ronan Cummins & Colm O’riordan

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  1. Ronan Cummins
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  2. Colm O’riordan
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Correspondence to Ronan Cummins.

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Cummins, R., O’riordan, C. Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections. Artif Intell Rev 24, 277–299 (2005). https://doi.org/10.1007/s10462-005-9001-y

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