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A System for Uncovering Latent Connectivity of Health Care Providers in Online Reviews

  • Frederik S. BäumerEmail author
  • Michaela Geierhos
  • Sabine Schulze
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 538)

Abstract

The contacts a health care provider (HCP), like a physician, has to other HCPs is perceived as a quality characteristic by patients. So far, only the German physician rating website jameda.de gives information about the interconnectedness of HCPs in business networks. However, this network has to be maintained manually and is thus incomplete. We therefore developed a system for uncovering latent connectivity of HCPs in online reviews to provide users with more valuable information about their HCPs. The overall goal of this approach is to extend already existing business networks of HCPs by integrating connections that are newly discovered by our system. Our most recent evaluation results are promising: 70.8 % of the connections extracted from the reviews texts were correctly identified and in total 3,788 relations were recognized that have not been displayed in jameda.de’s network before.

Keywords

Latent connectivity Person named entity recognition and disambiguation Health care provider reviews 

Notes

Acknowledgments

Special thanks go to our student assistant Markus Dollmann who contributed to this project. Funding has been granted in part by the University of Paderborn and by the Ministry of Innovation, Higher Education and Research of North Rhine-Westphalia, Germany.

References

  1. 1.
    Abdallah, S., Shaalan, K., Shoaib, M.: Integrating rule-based system with classification for Arabic named entity recognition. In: Gelbukh, A. (ed.) CICLing 2012, Part I. LNCS, vol. 7181, pp. 311–322. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Alhelbawy, A., Gaizauskas, R.: Named entity disambiguation using HMMs. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 159–162 (November 2013)Google Scholar
  3. 3.
    Alhelbawy, A., Gaizauskas, R.: Collective named entity disambiguation using graph ranking and clique partitioning approaches. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 1544–1555 (2014)Google Scholar
  4. 4.
    Alhelbawy, A., Gaizauskas, R.: Graph ranking for collective named entity disambiguation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 75–80. ACL (June 2014)Google Scholar
  5. 5.
    Chieu, H., Ng, H.: Named entity recognition: a maximum entropy approach using global information. In: COLING 2002: The 19th International Conference on Computational Linguistics, vol. 1, pp. 190–196. ACL (2002)Google Scholar
  6. 6.
    Chiticariu, L., Krishnamurthy, R., Li, Y., Reiss, F., Vaithyanathan, S.: Domain adaptation of rule-based annotators for named-entity recognition tasks. In: Proceedings of the 2010 Conference on EMNLP, pp. 1002–1012. ACL (2010)Google Scholar
  7. 7.
    Cucerzan, S., Yarowsky, D.: Language independent named entity recognition combining morphological and contextual evidence. In: Proceedings of the 1999 Joint SIGDAT Conference on EMNLP and VLC, pp. 90–99 (1999)Google Scholar
  8. 8.
    Derczynski, L., Maynard, D., Rizzo, G., van Erp, M., Gorrell, G., Troncy, R., Petrak, J., Bontcheva, K.: Analysis of named entity recognition and linking for Tweets. Inf. Process. Manage. 51(2), 32–49 (2015)CrossRefGoogle Scholar
  9. 9.
    Drozdzynski, W., Krieger, H., Piskorski, J., Schäfer, U., Xu, F.: Shallow processing with unification and typed feature structures – foundations and applications. Künstliche Intelligenz (KI) 18(1), 17–23 (2004)Google Scholar
  10. 10.
    Emmert, M., Meier, F.: An analysis of online evaluations on a physician rating website: evidence from a German public reporting instrument. J. Med. Internet Res. 15(8), 161–167 (2013)CrossRefGoogle Scholar
  11. 11.
    Emmert, M., Meier, F., Heider, A.K., Dürr, C., Sander, U.: What do patients say about their physicians? An analysis of 3000 narrative comments posted on a German physician rating website. Health Policy 118(1), 66–73 (2014)CrossRefGoogle Scholar
  12. 12.
    Fülöp, G., Kopetsch, T., Schöpe, P.: Einzugsbereiche von Arztpraxen und die Rolle der räumlichen Distanz für die Arztwahl der Patienten. In: Strobl, J., Blaschke, T., Griesebner, G. (eds.) Angewandte Geoinformatik 2009: Beiträge zum 21. AGIT- Symposium Salzburg, pp. 218–227. Wichmann, Heidelberg (2009)Google Scholar
  13. 13.
    Geierhos, M.: Grammatik der Menschenbezeichner in biographischen Kontexten, Arbeiten zur Informations- und Sprachverarbeitung, vol. 2. Centrum für Informations- und Sprachverarbeitung, Munich (2007)Google Scholar
  14. 14.
    Geierhos, M., Bäumer, F.S., Schulze, S., Stuß, V.: Filtering reviews by random individual error. In: Ali, M., Kwon, Y.S., Lee, C.-H., Kim, J., Kim, Y. (eds.) IEA/AIE 2015. LNCS, vol. 9101, pp. 305–315. Springer, Heidelberg (2015)Google Scholar
  15. 15.
    Geraedts, M.: Informationsbedarf und Informationssuchverhalten bei der Arztsuche. In: Böcken, J., Braun, B., Amhof, R. (eds.) Gesundheitsmonitor 2008: Gesundheitsversorgung und Gestaltungsoptionen aus der Perspektive der Bevölkerung, pp. 29–47. Bertelsmann Stiftung, Gütersloh (2008)Google Scholar
  16. 16.
    Grishman, R.: Information extraction. In: Clark, A., Fox, C., Lappin, S. (eds.) The Handbook of Computational Linguistics and Natural Language Processing, pp. 517–530. Wiley, New York, NY (2013)Google Scholar
  17. 17.
    Gross, M.: Local grammars. In: Roche, E., Schabes, Y. (eds.) Finite-State Language Processing, pp. 330–354. MIT Press, Cambridge (1997)Google Scholar
  18. 18.
    Kazama, J., Torisawa, K.: Exploiting Wikipedia as external knowledge for named entity recognition. In: Proceedings of the 2007 Joint Conference on EMNLP- CoNLL, pp. 698–707. ACL (June 2007)Google Scholar
  19. 19.
    Khalid, M.A., Jijkoun, V., de Rijke, M.: The impact of named entity normalization on information retrieval for question answering. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 705–710. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Klahold, A.: Empfehlungssysteme: Recommender Systems - Grundlagen. Konzepte und Lösungen. Vieweg + Teubner in GWV Fachverlage, Wiesbaden (2009)CrossRefGoogle Scholar
  21. 21.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  22. 22.
    McCallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of the 7th CoNLL at HLT-NAACL, vol. 4, pp. 188–191. ACL (2003)Google Scholar
  23. 23.
    McDonald, D.D.: Internal and external evidence in the identification and semantic categorization of proper names. In: Boguraev, B., Pustejovsky, J. (eds.) Corpus Processing for Lexical Acquisition, pp. 21–39. MIT Press, Cambridge (1996)Google Scholar
  24. 24.
    Mikheev, A., Moens, M., Grover, C.: Named entity recognition without gazetteers. In: Proceedings of the 9th Conference of the European ACL, pp. 1–8. ACL (1999)Google Scholar
  25. 25.
    Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. TACL 2, 231–244 (2014)Google Scholar
  26. 26.
    Paumier, S.: UNITEX 3.1. BETA USER MANUAL (2015). http://igm.univ-mlv.fr/~unitex/UnitexManual3.1.pdf. Accessed 18 May 2015
  27. 27.
    Ritter, A., Clark, S., Etzioni, M., Etzioni, O.: Named entity recognition in Tweets: an experimental study. In: Proceedings of the Conference on EMNLP, pp. 1524–1534. ACL, Edinburgh, UK (2011)Google Scholar
  28. 28.
    Scherfer, K., Pieplow, B.: Methoden der Webwissenschaft. Teil 1, Schriftenreihe Webwissenschaft, vol. 2. LIT Verlag Münster (2013)Google Scholar
  29. 29.
    Sil, A., Yates, A.: Re-ranking for joint named-entity recognition and linking. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2369–2374. ACM (2013)Google Scholar
  30. 30.
    Zhou, G., Su, J.: Named entity recognition using an hmm-based chunk tagger. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 473–480. ACL (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Frederik S. Bäumer
    • 1
    Email author
  • Michaela Geierhos
    • 1
  • Sabine Schulze
    • 1
  1. 1.Heinz Nixdorf InstituteUniversity of PaderbornPaderbornGermany

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