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IRIT-QFR: IRIT Query Feature Resource

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10456))

Abstract

In this paper, we present a resource that consists of query features associated with TREC adhoc collections. We developed two types of query features: linguistics features that can be calculated from the query itself, prior to any search although some are collection-dependent and post-retrieval features that imply the query has been evaluated over the target collection. This paper presents the two types of features that we have estimated as well as their variants, and the resource produced. The total number of features with their variants that we have estimated is 258 where the number of pre-retrieval and post-retrieval features are 81 and 171, respectively. We also present the first analysis of this data that shows that some features are more relevant than others in IR applications. Finally, we present a few applications in which these resources could be used although the idea of making them available is to foster new usages for IR.

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Notes

  1. 1.

    [10] paper presents the SynSet feature which is one of the features based on WordNet and thus included in Sect. 2.1.

  2. 2.

    http://terrier.org/docs/v4.0/learning.html.

  3. 3.

    https://www.microsoft.com/en-us/research/project/mslr/.

  4. 4.

    http://trec.nist.gov/data/robust.html.

References

  1. Baayen, R.H., Piepenbrock, R., Gulikers, L.: The Celex Lexical Database (Release 2). Linguistic Data Consortium, Philadelphia (1995)

    Google Scholar 

  2. Carmel, D., Yom-Tov, E.: Estimating the query difficulty for information retrieval. Synth. Lect. Inf. Concepts Retr. Serv. 2(1), 1–89 (2010)

    MATH  Google Scholar 

  3. Clarke, C.L., Craswell, N., Soboroff, I.: Overview of the TREC 2004 terabyte track. In: TREC, vol. 4, p. 74 (2004)

    Google Scholar 

  4. Deveaud, R., Mothe, J., Nie, J.-Y.: Learning to rank system configurations. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2001–2004. ACM (2016)

    Google Scholar 

  5. Hauff, C., Murdock, V., Baeza-Yates, R.: Improved query difficulty prediction for the web. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 439–448. ACM (2008)

    Google Scholar 

  6. Hawking, D.: Overview of the TREC-9 web track. In: TREC (2000)

    Google Scholar 

  7. Macdonald, C., Santos, R.L., Ounis, I., He, B.: About learning models with multiple query-dependent features. ACM Trans. Inf. Syst. (TOIS) 31(3), 11 (2013)

    Article  Google Scholar 

  8. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)

    Google Scholar 

  9. Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  10. Mothe, J., Tanguy, L.: Linguistic features to predict query difficulty. In: ACM Conference on Research and Development in Information Retrieval, SIGIR, Predicting Query Difficulty-Methods and Applications Workshop, pp. 7–10 (2005)

    Google Scholar 

  11. Mothe, J., Washha, M.: Predicting the best system parameter configuration: the (per parameter learning) PPL method. In: 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (2017)

    Google Scholar 

  12. Qin, T., Liu, T.-Y., Xu, J., Li, H.: Letor: a benchmark collection for research on learning to rank for information retrieval. Inf. Retr. 13(4), 346–374 (2010)

    Article  Google Scholar 

  13. Shtok, A., Kurland, O., Carmel, D.: Predicting query performance by query-drift estimation. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 305–312. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04417-5_30

    Chapter  Google Scholar 

  14. Voorhees, E.M.: The TREC robust retrieval track. In: ACM SIGIR Forum, vol. 39, pp. 11–20. ACM (2005)

    Google Scholar 

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Correspondence to Josiane Mothe .

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Molina, S., Mothe, J., Roques, D., Tanguy, L., Ullah, M.Z. (2017). IRIT-QFR: IRIT Query Feature Resource. In: Jones, G., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2017. Lecture Notes in Computer Science(), vol 10456. Springer, Cham. https://doi.org/10.1007/978-3-319-65813-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-65813-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65812-4

  • Online ISBN: 978-3-319-65813-1

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