nach oben

vorheriges Kapitel Nonlinearity Estimation of Digital Signals

nächstes Kapitel Intelligent System for Generation and Evaluatio...

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2020 | OriginalPaper | Buchkapitel

Aggregation on Learning to Rank for Consumer Health Information Retrieval

Erstes Kapitel lesen

Autoren: Hua Yang, Teresa Gonçalves

Verlag: Springer International Publishing

Erschienen in: Modelling and Development of Intelligent Systems

» Jetzt Zugang zum Volltext erhalten

Abstract

Common people are increasingly acquiring health information depending on general search engines which are still far from being effective in dealing with complex consumer health queries. One prime and effective method in addressing this problem is using Learning to Rank (L2R) techniques. In this paper, an investigation on aggregation over field-based L2R models is made. Rather than combining all potential features into one list to train a L2R model, we propose to train a set of L2R models each using features extracted from only one field and then apply aggregation methods to combine the results obtained from each model. Extensive experimental comparisons with the state-of-the-art baselines on the considered data collections confirmed the effectiveness of our proposed approach.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Jetzt einloggen Kostenlos registrieren

Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 69.000 Bücher
über 500 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Umwelt
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 50.000 Bücher
über 380 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Umwelt
Maschinenbau + Werkstoffe

Testen Sie jetzt 30 Tage kostenlos.

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 58.000 Bücher
über 300 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Jetzt informieren

https://sites.google.com/site/clefehealth/.

Sort-based combination is also known as rank-based combination in the literature.

These are the texts that do not belong to Title or H1.

We regard the full text as one field information as well.

https://www.reddit.com/r/AskDocs/.

http://lemurproject.org/clueweb12/.

http://commoncrawl.org.

https://www.hon.ch/.

http://terrier.org/.

Abacha, A.B.: NLM NIH at TREC 2016 clinical decision support track. In: TREC (2016)

Ai, Q., Bi, K., Luo, C., Guo, J., Croft, W.B.: Unbiased learning to rank with unbiased propensity estimation. arXiv preprint arXiv:1804.05938 (2018)

Amati, G.: Probabilistic models for information retrieval based on divergence from randomness. Ph.D. thesis, University of Glasgow, UK (2003)

Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 276–284. ACM (2001)

Chapelle, O., Chang, Y.: Yahoo! learning to rank challenge overview. In: Proceedings of the Learning to Rank Challenge, pp. 1–24 (2011)

Deng, K., Han, S., Li, K.J., Liu, J.S.: Bayesian aggregation of order-based rank data. J. Am. Stat. Assoc. 109(507), 1023–1039 (2014) MathSciNetCrossRef

Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622. ACM (2001)

Fox, E.A., Shaw, J.A.: Combination of multiple searches. NIST special publication SP 243 (1994)

Fox, S., Duggan, M.: Health online 2013. Pew Internet & American Life Project, Washington, DC (2013)

10.

Goeuriot, L., Jones, G.J., Kelly, L., Müller, H., Zobel, J.: Medical information retrieval: introduction to the special issue. Inf. Retr. J. 1(19), 1–5 (2016)

11.

Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002) CrossRef

12.

Jimmy, Zuccon, G., Palotti, J.: Overview of the CLEF 2018 consumer health search task. In: Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings (2018)

13.

Kuzi, S., Shtok, A., Kurland, O.: Query expansion using word embeddings. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1929–1932. ACM (2016)

14.

Lee, J.H.: Analyses of multiple evidence combination. In: ACM SIGIR Forum, vol. 31-SI, pp. 267–276. ACM (1997)

15.

Liu, T.Y., Xu, J., Qin, T., Xiong, W., Li, H.: LETOR: benchmark dataset for research on learning to rank for information retrieval. In: Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, vol. 310. ACM, Amsterdam (2007)

16.

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) CrossRef

17.

Manmatha, R., Rath, T., Feng, F.: Modeling score distributions for combining the outputs of search engines. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 267–275. ACM (2001)

18.

Montague, M., Aslam, J.A.: Relevance score normalization for metasearch. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 427–433. ACM (2001)

19.

Palotti, J., Goeuriot, L., Zuccon, G., Hanbury, A.: Ranking health web pages with relevance and understandability. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 965–968. ACM (2016)

20.

Palotti, J., Rekabsaz, N.: Exploring understandability features to personalize consumer health search. In: CEUR-WS, Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum (2018)

21.

Palotti, J., et al.: CLEF 2017 task overview: the IR task at the ehealth evaluation lab. In: Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings (2017)

22.

Roberts, K., Simpson, M.S., Voorhees, E.M., Hersh, W.R.: Overview of the TREC 2015 clinical decision support track. In: TREC (2015)

23.

Scells, H., Zuccon, G., Deacon, A., Koopman, B.: QUT ielab at CLEF ehealth 2017 technology assisted reviews track: Initial experiments with learning to rank. In: CEUR Workshop Proceedings: Working Notes of CLEF 2017: Conference and Labs of the Evaluation Forum. vol. 1866, Paper-98. CEUR Workshop Proceedings (2017)

24.

Jo, S.-H., Lee, K.S.: CBNU at TREC 2016 clinical decision support track. In: Text Retrieval Conference (TREC 2016) (2016)

25.

Soldaini, L., Goharian, N.: Learning to rank for consumer health search: a semantic approach. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 640–646. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_60 CrossRef

26.

Song, Y., He, Y., Hu, Q., He, L., Haacke, E.M.: ECNU at 2015 ehealth task 2: user-centred health information retrieval. In: CLEF (Working Notes) (2015)

27.

Tax, N., Bockting, S., Hiemstra, D.: A cross-benchmark comparison of 87 learning to rank methods. Inf. Process. Manage. 51(6), 757–772 (2015) CrossRef

28.

Thuma, E., Anderson, G., Mosweunyane, G.: UBML participation to CLEF ehealth IR challenge 2015: Task 2. In: CLEF (Working Notes) (2015)

29.

Vogt, C.C., Cottrell, G.W.: Predicting the performance of linearly combined IR systems. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 190–196. ACM (1998)

30.

Vogt, C.C., Cottrell, G.W.: Fusion via a linear combination of scores. Inf. Retrieval 1(3), 151–173 (1999) CrossRef

31.

Wang, H., Langley, R., Kim, S., McCord-Snook, E., Wang, H.: Efficient exploration of gradient space for online learning to rank. arXiv preprint arXiv:1805.07317 (2018)

32.

Wang, R., Lu, W., Ren, K.: WHUIRgroup at the CLEF 2016 ehealth lab task 3. In: CLEF (Working Notes), pp. 193–197 (2016)

33.

Wang, X., Bendersky, M., Metzler, D., Najork, M.: Learning to rank with selection bias in personal search. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 115–124. ACM (2016)

34.

Xia, X., Lo, D., Wang, X., Zhang, C., Wang, X.: Cross-language bug localization. In: Proceedings of the 22nd International Conference on Program Comprehension, pp. 275–278. ACM (2014)

35.

Yang, H., Goncalves, T.: Promoting understandability in consumer health information search. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 727–734. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_72 CrossRef

36.

Zuccon, G., et al.: The IR task at the CLEF ehealth evaluation lab 2016: user-centred health information retrieval. In: CLEF 2016-Conference and Labs of the Evaluation Forum, vol. 1609, pp. 15–27 (2016)

Titel

Aggregation on Learning to Rank for Consumer Health Information Retrieval

Buch

Modelling and Development of Intelligent Systems

Print ISBN: 978-3-030-39236-9

Electronic ISBN: 978-3-030-39237-6

https://doi.org/10.1007/978-3-030-39237-6

DOI

https://doi.org/10.1007/978-3-030-39237-6_6

Autoren:

Hua Yang
Teresa Gonçalves

Verlag

Springer International Publishing

Sequenznummer

Premium Partner

Bildnachweise