[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12656))

Included in the following conference series:

Abstract

Professional search queries are often formulated in a structured manner, where multiple aspects are combined in a logical form. The information need is often fulfilled by an initial retrieval stage followed by a complex reranking algorithm. In this paper, we analyze a simple, explainable reranking model that follows the structured search criterion. Different aspects of the criterion are predicted by machine learning classifiers, which are then combined through the logical form to predict document relevance. On three years of data from the TREC Precision Medicine literature search track (2017–2019), we show that the simple model consistently performs as well as LambdaMART rerankers. Furthermore, many black-box rerankers developed by top-ranked TREC teams can be replaced by this simple model without statistically significant performance change. Finally, we find that the model can achieve remarkably high performance even when manually labeled documents are very limited. Together, these findings suggest that leveraging the structure in professional search queries is a promising direction towards building explainable, label-efficient, and high-performance retrieval models for professional search tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 87.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 109.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agosti, M., Nunzio, G.M.D., Marchesin, S.: The university of Padua IMS research group at TREC 2018 precision medicine track (2018)

    Google Scholar 

  2. Aromataris, E., Riitano, D.: Systematic reviews: constructing a search strategy and searching for evidence. Am. J. Nurs. 114(5), 49–56 (2014)

    Article  Google Scholar 

  3. Beltagy, I., Lo, K., Cohan, A.: Scibert: a pretrained language model for scientific text. arXiv preprint arXiv:1903.10676 (2019)

  4. Burges, C.J.: From ranknet to lambdarank to lambdamart: an overview. Learning 11(23–581), 81 (2010)

    Google Scholar 

  5. Caucheteur, D., Pasche, E., Gobeill, J., Mottaz, A., Mottin, L., Ruch, P.: Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in precision medicine. In: TREC (2019)

    Google Scholar 

  6. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  7. Cieslewicz, A., Dutkiewicz, J., Jedrzejek, C.: Poznan contribution to TREC-PM 2019. In: TREC (2019)

    Google Scholar 

  8. Dang, V., Bendersky, M., Croft, W.B.: Two-stage learning to rank for information retrieval. In: European Conference on Information Retrieval, pp. 423–434 (2013)

    Google Scholar 

  9. Faessler, E., Hahn, U., Oleynik, M.: Julie lab & med uni graz@ TREC 2019 precision medicine track. In: TREC (2019)

    Google Scholar 

  10. Faessler, E., Oleynik, M., Hahn, U.: What makes a top-performing precision medicine search engine? tracing main system features in a systematic way. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 459–468 (2020)

    Google Scholar 

  11. Feng, J., Yang, Z., Liu, Z., Luo, L., Lin, H., Wang, J.: Dutir at TREC 2019: Precision medicine track. In: TREC (2019)

    Google Scholar 

  12. Fernando, Z.T., Singh, J., Anand, A.: A study on the interpretability of neural retrieval models using deepshap. In: SIGIR 2019. pp. 1005–1008. ACM, New York, NY, USA (2019)

    Google Scholar 

  13. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  Google Scholar 

  14. Guo, J., Fan, Y., Ai, Q., Croft, W.B.: A deep relevance matching model for ad-hoc retrieval. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 55–64 (2016)

    Google Scholar 

  15. Hui, K., Yates, A., Berberich, K., de Melo, G.: PACRR: a position-aware neural IR model for relevance matching. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1049–1058. Association for Computational Linguistics, Copenhagen, Denmark (September 2017)

    Google Scholar 

  16. Kanoulas, E., Li, D., Azzopardi, L., Spijker, R.: Clef 2019 technology assisted reviews in empirical medicine overview. In: CEUR Workshop Proceedings, vol. 2380 (2019)

    Google Scholar 

  17. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

    Google Scholar 

  18. LexisNexis: Developing a search with lexisnexis. Accessed September 2020). http://www.lexisnexis.com/bis-user-information/docs/developingasearch.pdf

  19. Li, P., Wu, Q., Burges, C.J.: Mcrank: learning to rank using multiple classification and gradient boosting. In: Advances in Neural Information Processing Systems, pp. 897–904 (2008)

    Google Scholar 

  20. Liu, T.Y.: Learning to Rank for Information Retrieval. Springer, Beijing (2011) https://doi.org/10.1007/978-3-642-1467-3

  21. Liu, X., Li, L., Yang, Z., Dong, S.: SCUT-CCNL at TREC 2019 precision medicine track. In: TREC (2019)

    Google Scholar 

  22. López-Úbeda, P., Vera-Ramos, J.A., López-García, P.: TREC 2019 precision medicine - medical university of Graz. In: TREC (2019)

    Google Scholar 

  23. Nunzio, G.M.D., Marchesin, S., Agosti, M.: Exploring how to combine query reformulations for precision medicine. In: TREC (2019)

    Google Scholar 

  24. Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Johnson, D.: Terrier information retrieval platform. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 517–519. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31865-1_37

    Chapter  Google Scholar 

  25. O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., Ananiadou, S.: Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst. Rev. 4(1), 5 (2015)

    Article  Google Scholar 

  26. Qu, J., Arguello, J., Wang, Y.: Towards explainable retrieval models for precision medicine literature search. In: Proceedings of the 43rd ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1593–1596 (2020)

    Google Scholar 

  27. Qu, J., Wang, Y.: UNC SILS at TREC 2019 precision medicine track. In: TREC (2019)

    Google Scholar 

  28. Roberts, K., et al.: Overview of the TREC 2017 precision medicine track (2017)

    Google Scholar 

  29. Roberts, K., et al.: Overview of the TREC 2019 precision medicine track. In: TREC (2019)

    Google Scholar 

  30. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Mach. Intell. 1(5), 206–215 (2019)

    Article  Google Scholar 

  31. Russell-Rose, T., Chamberlain, J., Azzopardi, L.: Information retrieval in the workplace: a comparison of professional search practices. Inf. Process. Manage. 54(6), 1042–1057 (2018)

    Article  Google Scholar 

  32. Rybinski, M., Karimi, S., Paris, C.: Csiro at 2019 TREC precision medicine track. In: TREC (2019)

    Google Scholar 

  33. Schardt, C., Adams, M.B., Owens, T., Keitz, S., Fontelo, P.: Utilization of the pico framework to improve searching pubmed for clinical questions. BMC Med. Inf. Decis. Making 7(1), 16 (2007)

    Article  Google Scholar 

  34. Singh, J., Anand, A.: Posthoc interpretability of learning to rank models using secondary training data. arXiv:1806.11330 (2018)

  35. Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: CIKM, pp. 623–632 (2007)

    Google Scholar 

  36. Tian, A., Lease, M.: Active learning to maximize accuracy vs. effort in interactive information retrieval. In: Proceedings of the 34th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 145–154 (2011)

    Google Scholar 

  37. Wallace, B.C., Trikalinos, T.A., Lau, J., Brodley, C., Schmid, C.H.: Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinform. 11(1), 1–11 (2010)

    Article  Google Scholar 

  38. Wu, D.T.Y., Su, W., Lee, J.J.: Retrieving scientific abstracts using venue- and concept-based approaches: Cincymedir at TREC 2019 precision medicine track. In: TREC (2019)

    Google Scholar 

  39. Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. arXiv preprint: 1804.11192 (2018)

    Google Scholar 

  40. Zheng, Q., Li, Y., Hu, J., Yang, Y., He, L., Xue, Y.: ECNU-ICA team at TREC 2019 precision medicine track. In: TREC (2019)

    Google Scholar 

Download references

Acknowledgment

UNC SILS Kilgour Research Grant supported this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yue Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qu, J., Arguello, J., Wang, Y. (2021). A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72113-8_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72112-1

  • Online ISBN: 978-3-030-72113-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics