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Term Selection for Query Expansion in Medical Cross-Lingual Information Retrieval

  • Shadi SalehEmail author
  • Pavel Pecina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs machine translation of source-language queries into a document language and linear regression to predict the retrieval performance for each translated query when expanded with a candidate term. Candidate terms (in the document language) come from multiple sources: query translation hypotheses obtained from the machine translation system, Wikipedia articles and PubMed abstracts. Query expansion is applied only when the model predicts a score for a candidate term that exceeds a tuned threshold which allows to expand queries with strongly related terms only. Our experiments are conducted using the CLEF eHealth 2013–2015 test collection and show significant improvements in both cross-lingual and monolingual settings.

Notes

Acknowledgments

This work was supported by the Czech Science Foundation (grant n. 19-26934X).

References

  1. 1.
    Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24752-4_10CrossRefGoogle Scholar
  2. 2.
    Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of AMIA Symposium, pp. 17–21 (2001)Google Scholar
  3. 3.
    Cao, G., Nie, J.Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 243–250. ACM, New York (2008)Google Scholar
  4. 4.
    Chandra, G., Dwivedi, S.K.: Query expansion based on term selection for Hindi-English cross lingual IR. J. King Saud Univ. Comput. Inf. Sci. (2017)Google Scholar
  5. 5.
    Chiang, W.T.M., Hagenbuchner, M., Tsoi, A.C.: The wt10g dataset and the evolution of the web. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, WWW 2005, pp. 938–939. ACM, New York (2005)Google Scholar
  6. 6.
    Choi, S., Choi, J.: Exploring effective information retrieval technique for the medical web documents: Snumedinfo at clefehealth2014 task 3. In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation forum, vol. 1180, pp. 167–175. CEUR-WS.org, Sheffield (2014)Google Scholar
  7. 7.
    Dušek, O., Hajič, J., Hlaváčová, J., Novák, M., Pecina, P., Rosa, R., et al.: Machine translation of medical texts in the Khresmoi project. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 221–228, Baltimore (2014)Google Scholar
  8. 8.
    Ermakova, L., Mothe, J.: Query expansion by local context analysis. In: Conference francophone en Recherche d’Information et Applications (CORIA 2016), pp. 235–250. CORIA-CIFED, Toulouse (2016)Google Scholar
  9. 9.
    Gabrilovich, E., Broder, A., Fontoura, M., Joshi, A., Josifovski, V., Riedel, L., Zhang, T.: Classifying search queries using the web as a source of knowledge. ACM Trans. Web 3(2), 5 (2009)CrossRefGoogle Scholar
  10. 10.
    Goeuriot, L., et al.: ShARe/CLEF eHealth evaluation lab 2014, Task 3: user-centred health information retrieval. In: Proceedings of CLEF 2014, pp. 43–61. CEUR-WS.org, Sheffield (2014)Google Scholar
  11. 11.
    Goeuriot, L., et al.: Overview of the CLEF eHealth evaluation lab 2015. In: Mothe, J., et al. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 429–443. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24027-5_44CrossRefGoogle Scholar
  12. 12.
    Harman, D.: Towards interactive query expansion. In: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 321–331. SIGIR 1988, ACM, New York (1988)Google Scholar
  13. 13.
    Harman, D.: Information retrieval. In: Relevance Feedback and Other Query Modification Techniques, pp. 241–263. Prentice-Hall Inc., Upper Saddle River (1992)Google Scholar
  14. 14.
    Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 329–338. ACM, Pittsburgh (1993)Google Scholar
  15. 15.
    Humphreys, B.L., Lindberg, D.A.B., Schoolman, H.M., Barnett, G.O.: The unified medical language system. J. Am. Med. Inform. Assoc. 5(1), 1–11 (1998)CrossRefGoogle Scholar
  16. 16.
    Kalpathy-Cramer, J., Muller, H., Bedrick, S., Eggel, I., De Herrera, A., Tsikrika, T.: Overview of the clef 2011 medical image classification and retrieval tasks. In: CLEF 2011 - Working Notes for CLEF 2011 Conference, vol. 1177. CEUR-WS (2011)Google Scholar
  17. 17.
    Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Demo and Poster Sessions, pp. 177–180, Stroudsburg (2007)Google Scholar
  18. 18.
    Liu, X., Nie, J.: Bridging layperson’s queries with medical concepts - GRIUM @CLEF2015 eHealth Task 2. In: Working Notes of CLEF 2015 Conference and Labs of the Evaluation forum, vol. 1391. CEUR-WS.org, Toulouse (2015)Google Scholar
  19. 19.
    McCarley, J.S.: Should we translate the documents or the queries in cross-language information retrieval? In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 208–214, College Park (1999)Google Scholar
  20. 20.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2, pp. 3111–3119. Curran Associates Inc., Red Hook (2013)Google Scholar
  21. 21.
    Nikoulina, V., Kovachev, B., Lagos, N., Monz, C.: Adaptation of statistical machine translation model for cross-lingual information retrieval in a service context. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 109–119, Stroudsburg (2012)Google Scholar
  22. 22.
    Nogueira, R., Cho, K.: Task-oriented query reformulation with reinforcement learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 574–583 (2017)Google Scholar
  23. 23.
    Nunzio, G.M.D., Moldovan, A.: A study on query expansion with mesh terms and elasticsearch. IMS unipd at CLEF ehealth task 3. In: Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, 10–14 September 2018. CEUR-WS, Avignon (2018)Google Scholar
  24. 24.
    Oard, D.W.: A comparative study of query and document translation for cross-language information retrieval. In: Farwell, D., Gerber, L., Hovy, E. (eds.) AMTA 1998. LNCS (LNAI), vol. 1529, pp. 472–483. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-49478-2_42CrossRefGoogle Scholar
  25. 25.
    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_37CrossRefGoogle Scholar
  26. 26.
    Pal, D., Mitra, M., Datta, K.: Improving query expansion using wordnet. J. Assoc. Inf. Sci. Technol. 65(12), 2469–2478 (2014)CrossRefGoogle Scholar
  27. 27.
    Palotti, J.R., Zuccon, G., Goeuriot, L., Kelly, L., Hanbury, A., Jones, G.J., Lu pu, M., Pecina, P.: CLEF eHealth Evaluation Lab 2015, Task 2: Retrieving information about medical symptoms. In: CLEF (Working Notes), pp. 1–22. Springer, Heidelberg (2015)Google Scholar
  28. 28.
    Pecina, P., Dušek, O., Goeuriot, L., Hajič, J., Hlavářová, J., Jones, G.J., et al.: Adaptation of machine translation for multilingual information retrieval in the medical domain. Artif. Intell. Med. 61(3), 165–185 (2014)CrossRefGoogle Scholar
  29. 29.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Peng, Y., Wei, C.H., Lu, Z.: Improving chemical disease relation extraction with rich features and weakly labeled data. J. Cheminformatics 8(1), 53 (2016)CrossRefGoogle Scholar
  31. 31.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  32. 32.
    Pirkola, A., Hedlund, T., Keskustalo, H., Järvelin, K.: Dictionary-based cross-language information retrieval: problems, methods, and research findings. Inform. Retrieval 4(3–4), 209–230 (2001)CrossRefGoogle Scholar
  33. 33.
    Rocchio, J.J.: Relevance feedback in information retrieval. The SMART Retrieval Syst. Exp. Autom. Doc. Process. 313–323 (1971)Google Scholar
  34. 34.
    Saleh, S., Pecina, P.: Reranking hypotheses of machine-translated queries for cross-lingual information retrieval. In: Fuhr, N., Quaresma, P., Gonçalves, T., Larsen, B., Balog, K., Macdonald, C., Cappellato, L., Ferro, N. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 54–66. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-44564-9_5CrossRefGoogle Scholar
  35. 35.
    Saleh, S., Pecina, P.: Task3 patient-centred information retrieval: Team CUNI. In: Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum. CEUR-WS.org, Evora (2016)Google Scholar
  36. 36.
    Saleh, S., Pecina, P.: An Extended CLEF eHealth Test Collection for Cross-lingual Information Retrieval in the medical domain. In: Advances in Information Retrieval - 41th European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings. Lecture Notes in Computer Science, Springer (2019)Google Scholar
  37. 37.
    Smucker, M.D., Allan, J.: An investigation of Dirichlet prior smoothing’s performance advantage. University of Massachusetts, Technical report (2005)Google Scholar
  38. 38.
    Suominen, H., et al.: Overview of the ShARe/CLEF eHealth evaluation lab 2013. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 212–231. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40802-1_24CrossRefGoogle Scholar
  39. 39.
    Wright, T.B., Ball, D., Hersh, W.: Query expansion using mesh terms for dataset retrieval: OHSU at the biocaddie 2016 dataset retrieval challenge. J. Biol. Databases Curation 2017, Database (2017)Google Scholar
  40. 40.
    Zamani, H., Croft, W.B.: Embedding-based query language models. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, ICTIR 2016, pp. 147–156. ACM, New York (2016)Google Scholar
  41. 41.
    Zamani, H., Croft, W.B.: Relevance-based word embedding. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 505–514. SIGIR 2017. ACM, New York (2017)Google Scholar
  42. 42.
    Zuccon, G., Koopman, B., Bruza, P., Azzopardi, L.: Integrating and evaluating neural word embeddings in information retrieval. In: Proceedings of the 20th Australasian Document Computing Symposium, p. 12. Stroudsburg (2015)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Formal and Applied Linguistics, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

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