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Relevance weighting of search terms

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  • S. E. Robertson
  • K. Sparck Jones

Abstract

This paper examines statistical techniques for exploiting relevance information to weight search terms. These techniques are presented as a natural extension of weighting methods using information about the distribution of index terms in documents in general. A series of relevance weighting functions is derived and is justified by theoretical considerations. In particular, it is shown that specific weighted search methods are implied by a general probabilistic theory of retrieval. Different applications of relevance weighting are illustrated by experimental results for test collections.

Suggested Citation

  • S. E. Robertson & K. Sparck Jones, 1976. "Relevance weighting of search terms," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(3), pages 129-146, May.
    • Handle: RePEc:bla:jamest:v:27:y:1976:i:3:p:129-146
    DOI: 10.1002/asi.4630270302
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    Cited by:

    1. Edward Kai Fung Dang & Robert Wing Pong Luk & James Allan, 2022. "A retrieval model family based on the probability ranking principle for ad hoc retrieval," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1140-1154, August.
    2. Yunlong Ma & Hongfei Lin, 2014. "A Multiple Relevance Feedback Strategy with Positive and Negative Models," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    3. Victoria Yoon & Bonnie Rubenstein Montano & Teresa Wilson & Stuart Lowry & Jay Liebowitz, 2004. "Natural language interface for multi‐agent contracting system (MACS)," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(3), pages 153-165, July.
    4. Jiwon Yu & Jong-Gyu Hwang & Jumi Hwang & Sung Chan Jun & Sumin Kang & Chulung Lee & Hyundong Kim, 2020. "Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    5. Josiane Mothe, 2022. "Analytics Methods to Understand Information Retrieval Effectiveness—A Survey," Mathematics, MDPI, vol. 10(12), pages 1-25, June.
    6. Müge Akbulut & Yaşar Tonta & Howard D. White, 2020. "Related records retrieval and pennant retrieval: an exploratory case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 957-987, February.
    7. Jean-Charles Lamirel & Shadi Al Shehabi & Claire Francois & Xavier Polanco, 2004. "Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 427-441, November.
    8. Jean-Charles Lamirel, 2012. "A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 151-166, October.
    9. Jerry Ellig & Patrick A. McLaughlin, 2016. "The Regulatory Determinants of Railroad Safety," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 49(2), pages 371-398, September.
    10. Lynda Tamine & Cécile Chouquet & Thomas Palmer, 2015. "Analysis of biomedical and health queries: Lessons learned from TREC," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2626-2642, December.
    11. Kevin W Boyack & David Newman & Russell J Duhon & Richard Klavans & Michael Patek & Joseph R Biberstine & Bob Schijvenaars & André Skupin & Nianli Ma & Katy Börner, 2011. "Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    12. Huan Wang & Jian Li & Jiapeng Wang, 2023. "Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm," Mathematics, MDPI, vol. 11(18), pages 1-18, September.
    13. Guozhong Feng & Baiguo An & Fengqin Yang & Han Wang & Libiao Zhang, 2017. "Relevance popularity: A term event model based feature selection scheme for text classification," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-15, April.
    14. Jean-Charles Lamirel & Claire Francois & Shadi Al Shehabi & Martial Hoffmann, 2004. "New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 445-562, August.
    15. Georgia Warren-Myers & Monique Schmidt, 2023. "The Evolving Nature (or Not) of Sustainability Communications in New Home Building in Australia," Sustainability, MDPI, vol. 15(19), pages 1-20, September.
    16. Alexandra Dumitrescu & Simone Santini, 2021. "Full coverage of a reader's interests in context‐based information filtering," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1011-1027, August.

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