Advertisement

An Axiomatic Study of Query Terms Order in Ad-Hoc Retrieval

  • Ayyoob ImaniEmail author
  • Amir Vakili
  • Ali Montazer
  • Azadeh Shakery
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

Classic retrieval methods use simple bag-of-word representations for queries and documents. This representation fails to capture the full semantic richness of queries and documents. More recent retrieval models have tried to overcome this deficiency by using approaches such as incorporating dependencies between query terms, using bi-gram representations of documents, proximity heuristics, and passage retrieval. While some of these previous works have implicitly accounted for term order, to the best of our knowledge, term order has not been the primary focus of any research. In this paper, we will show that documents that have two query terms in the same order as in the query have a higher probability of being relevant than documents that have two query terms in the reverse order. Using the axiomatic framework for information retrieval, we introduce a constraint that retrieval models must adhere to in order to effectively utilize term order dependency among query terms. We modify two existing robust retrieval models based on this constraint. Our empirical evaluation using both TREC newswire and web corpora demonstrates that the modified retrieval models significantly outperform their original counterparts.

Keywords

Query term order Axiomatic analysis SDM PLM 

References

  1. 1.
    Bendersky, M., Croft, W.B.: Modeling higher-order term dependencies in information retrieval using query hypergraphs. In: Proceedings of the 35th ACM SIGIR Conference, pp. 941–950. ACM (2012)Google Scholar
  2. 2.
    Bendersky, M., Metzler, D., Croft, W.B.: Learning concept importance using a weighted dependence model. In: Proceedings of the Third ACM WSDM Conference, pp. 31–40 (2010)Google Scholar
  3. 3.
    Cummins, R., O’Riordan, C.: An axiomatic comparison of learned term-weighting schemes in information retrieval: clarifications and extensions. Artif. Intell. Rev. 28(1), 51–68 (2007)CrossRefGoogle Scholar
  4. 4.
    Fang, H., Tao, T., Zhai, C.: A formal study of information retrieval heuristics. In: Proceedings of the 27th Annual ACM SIGIR Conference, pp. 49–56. ACM (2004)Google Scholar
  5. 5.
    Fang, H., Zhai, C.: Semantic term matching in axiomatic approaches to information retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 115–122. ACM (2006)Google Scholar
  6. 6.
    Huston, S., Croft, W.B.: A comparison of retrieval models using term dependencies. In: Proceedings of the 23rd ACM CIKM, pp. 111–120. ACM (2014)Google Scholar
  7. 7.
    Lv, Y., Zhai, C.: Positional language models for information retrieval. In: Proceedings of the 32nd ACM SIGIR Conference, pp. 299–306. ACM (2009)Google Scholar
  8. 8.
    Metzler, D., Croft, W.B.: A Markov random field model for term dependencies. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 472–479. ACM (2005)Google Scholar
  9. 9.
    Montazeralghaem, A., Zamani, H., Shakery, A.: Axiomatic analysis for improving the log-logistic feedback model. In: Proceedings of the 39th ACM SIGIR Conference, pp. 765–768. SIGIR 2016. ACM (2016)Google Scholar
  10. 10.
    Peng, J., Macdonald, C., He, B., Plachouras, V., Ounis, I.: Incorporating term dependency in the DFR framework. In: Proceedings of the 30th ACM SIGIR Conference, pp. 843–844. ACM (2007)Google Scholar
  11. 11.
    Robertson, S.E., Walker, S.: Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 232–241. Springer, New York (1994).  https://doi.org/10.1007/978-1-4471-2099-5_24CrossRefGoogle Scholar
  12. 12.
    Yu, C.T., Buckley, C., Lam, K., Salton, G.: A generalized term dependence model in information retrieval. Technical report, Cornell University (1983)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ayyoob Imani
    • 1
    Email author
  • Amir Vakili
    • 1
  • Ali Montazer
    • 2
  • Azadeh Shakery
    • 1
  1. 1.University of TehranTehranIran
  2. 2.University of Massachusetts AmherstAmherstUSA

Personalised recommendations