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Improving Search Effectiveness with Field-based Relevance Modeling

Published:11 December 2018Publication History

ABSTRACT

Fields are a valuable auxiliary source of information in semi-structured HTML web documents. So, it is no surprise that ranking models have been designed to leverage this information to improve search effectiveness. We present the first (initial) study of utilizing field-based information in the relevance modeling framework. Fields play two different, and integrated, roles in our models: sources of information for inducing relevance models and units on which relevance models are applied for ranking. Our preliminary results suggest that field-based relevance modeling can improve precision at top ranks; specifically, to a greater extent than the commonly used BM25F and SDM-Fields field-based models. Further analysis shows that using field-based relevance models mainly improves the effectiveness of tail queries. Our findings suggest that using field-based information together with relevance modeling is a promising area of future exploration.

References

  1. N. Abdul-Jaleel, J. Allan, W. B. Croft, F. Diaz, L. Larkey, X. Li, M. D., and C. Wade. 2004. UMASS at TREC 2004 --- Novelty and HARD.Google ScholarGoogle Scholar
  2. F. Diaz. 2015. Condensed List Relevance Models. In Proc. ICTIR. 313--316. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Gallagher, R. Chen, J. Mackenzie, F. Scholer, R. Benham, and J. S. Culpepper. 2013. RMIT at the NTCIR-13 We Want Web Task. In Proc. NTCIR.Google ScholarGoogle Scholar
  4. J. Y. Kim and W. B. Croft. 2012. A Field Relevance Model for Structured Document Retrieval. In Proc. ECIR. 97--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. V. Lavrenko and W. B. Croft. 2001. Relevance Based Language Models. In Proc. SIGIR. 120--127. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Metzler and W. B. Croft. 2005. A Markov Random Field Model for Term Dependencies. In Proc. SIGIR. 472--479. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Metzler, T. Strohman, Y. Zhou, and W. B. Croft. 2005. Indri at TREC 2005: Terabyte Track. In Proc. TREC.Google ScholarGoogle Scholar
  8. H. R. Mohammad, K. Xu, J. Callan, and J. S. Culpepper. 2018. Dynamic Shard Cutoff Prediction for Selective Search. In Proc. SIGIR. 85--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. P. Ogilvie and J. P. Callan. 2003. Combining document representations for known-item search. In Proc. SIGIR. 143--150. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, M. Gatford, and others. 1994. Okapiat TREC-3. In Proc. TREC. 109--126.Google ScholarGoogle Scholar
  11. S. Robertson, H. Zaragoza, and M. Taylor. 2004. Simple BM25 Extension to Multiple Weighted Fields. In Proc. CIKM. 42--49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. Zamani, B. Mitra, X. Song, N. Craswell, and S. Tiwary. 2018. Neural Ranking Models with Multiple Document Fields. In Proc. WSDM. 700--708. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. H. Zaragoza, N. Craswell, M. Taylor, S. Saria, and S. Robertson. 2004. Microsoft Cambridge at TREC 13: Web and Hard Tracks. In Proc. TREC.Google ScholarGoogle Scholar
  1. Improving Search Effectiveness with Field-based Relevance Modeling

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    • Published in

      cover image ACM Other conferences
      ADCS '18: Proceedings of the 23rd Australasian Document Computing Symposium
      December 2018
      78 pages
      ISBN:9781450365499
      DOI:10.1145/3291992

      Copyright © 2018 ACM

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      New York, NY, United States

      Publication History

      • Published: 11 December 2018

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      • short-paper
      • Research
      • Refereed limited

      Acceptance Rates

      ADCS '18 Paper Acceptance Rate13of20submissions,65%Overall Acceptance Rate30of57submissions,53%

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