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Spanish Named Entity Recognition in the Biomedical Domain

  • Viviana CotikEmail author
  • Horacio Rodríguez
  • Jorge Vivaldi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)

Abstract

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.

Keywords

Named entity recognition Spanish Radiology reports BioNLP 

References

  1. 1.
    Aleksovski, Z.: Testing RadLex for completeness using large database of radiology reports. In: Society for Imaging Informatics in Medicine, Annual Meeting (2014)Google Scholar
  2. 2.
    Ambulódegui, E.S.: Manual de Terminología Médica N 2 (2012)Google Scholar
  3. 3.
    Ananiadou, S., Friedman, C., Tsujii, J.: Introduction: named entity recognition in biomedicine. J. Biomed. Inform. 37(6), 393–395 (2004)CrossRefGoogle Scholar
  4. 4.
    Basaldella, M., Furrer, L., Tasso, C., Rinaldi, F.: Entity recognition in the biomedical domain using a hybrid approach. J. Biomed. Semant. 8(1), 51 (2017)CrossRefGoogle Scholar
  5. 5.
    Batista-Navarro, R.T., Rak, R., Ananiadou, S.: Chemistry-specific features and heuristics for developing a CRF-based chemical named entity recogniser. In: Proceedings of the Fourth BioCreative Challenge Evaluation Workshop, vol. 2, pp. 55–59. Citeseer (2013)Google Scholar
  6. 6.
    Cascade, P.N., Berlin, L.: Malpractice issues in radiology. AJR Am. J. Roentgenol. 173(6), 1439–1442 (1999)CrossRefGoogle Scholar
  7. 7.
    Castro, E., Iglesias, A., Martínez, P., Castaño, L.: Automatic identification of biomedical concepts in Spanish-language unstructured clinical texts. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 751–757. ACM (2010)Google Scholar
  8. 8.
    Chapman, W.W., Cohen, K.B.: Current issues in biomedical text mining and natural language processing. J. Biomed. Inform. 42(5), 757–759 (2009)CrossRefGoogle Scholar
  9. 9.
    Chinchor, N., Hirschman, L., Lewis, D.D.: Evaluating message understanding systems: an analysis of the third message understanding conference (MUC-3). Assoc. Comput. Linguist. 19(3), 409–449 (1993)Google Scholar
  10. 10.
    Cohen, A.M., Hersh, W.R.: A survey of current work in biomedical text mining. Brief. Bioinform. 6(1), 57–71 (2005)CrossRefGoogle Scholar
  11. 11.
    Cotik, V., Filippo, D., Roller, R., Uszkoreit, H., Xu, F.: Annotation of entities and relations in Spanish radiology reports. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pp. 177–184 (2017)Google Scholar
  12. 12.
    Do, B., Wu, A., Maley, J., Biswal, S.: Automatic retrieval of bone fracture knowledge using natural language processing. J. Digit. Imaging 26(4), 709–713 (2013)CrossRefGoogle Scholar
  13. 13.
    Iglesias, A., et al.: Mostas: Un etiquetador morfo-semántico, anonimizador y corrector de historiales clínicos. Procesamiento del lenguaje Nat. 41, 299–300 (2008)Google Scholar
  14. 14.
    Jiang, J., Guan, Y., Zhao, C.: WI-ENRE in CLEF eHealth evaluation lab 2015: clinical named entity recognition based on CRF. In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation Forum, Toulouse, France (2015)Google Scholar
  15. 15.
    López Piñero, J.M., Terrada Ferrandis, M.L.: Introducción a la terminología médica. Masson S.A. (2005)Google Scholar
  16. 16.
    Laguna, J.Y.: Diccionario de siglas médicas y otras abreviaturas, epónimos y términos médicos relacionados con la codificación de las altas hospitalariasGoogle Scholar
  17. 17.
    Lakhani, P., Langlotz, C.P.: Automated detection of radiology reports that document non-routine communication of critical or significant results. J. Digit. Imaging 23(6), 647–57 (2009)CrossRefGoogle Scholar
  18. 18.
    Leaman, R., Gonzalez, G.: BANNER: an executable survey of advances in biomedical named entity recognition. In: Proceedings of the Pacific Symposium on Biocomputing, vol. 13, pp. 652–663 (2008)Google Scholar
  19. 19.
    Moon, S., Pakhomov, S.V.S., Liu, N., Ryan, J.O., Melton, G.B.: A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources. JAMIA 21(2), 299–307 (2014)Google Scholar
  20. 20.
    Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Linguist. Investig. 1(30), 3–26 (2007).  https://doi.org/10.1075/li.30.1.03nadCrossRefGoogle Scholar
  21. 21.
    Oronoz, M., Casillas, A., Gojenola, K., Perez, A.: Automatic annotation of medical records in Spanish with disease, drug and substance names. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 536–543. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41827-3_67CrossRefGoogle Scholar
  22. 22.
    Poibeau, T., Kosseim, L.: Proper name extraction from non-journalistic texts. In: Computational Linguistics in the Netherlands 2000, Selected Papers from the Eleventh CLIN Meeting, Tilburg, 3 November 2000, pp. 144–157 (2000)Google Scholar
  23. 23.
    Roller, R., et al.: Detecting named entities and relations in German clinical reports. In: Rehm, G., Declerck, T. (eds.) GSCL 2017. LNCS (LNAI), vol. 10713, pp. 146–154. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-73706-5_12CrossRefGoogle Scholar
  24. 24.
    Santiso, S., Casillas, A., Pérez, A., Oronoz, M.: Medical entity recognition and negation extraction: assessment of NegEx on health records in Spanish. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10208, pp. 177–188. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56148-6_15CrossRefGoogle Scholar
  25. 25.
    Settles, B.: Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings of the COLING 2004 International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications (NLPBA/BioNLP), COLING 2004. Association for Computational Linguistics, Stroudsburg (2004)Google Scholar
  26. 26.
    Shen, D., Zhang, J., Zhou, G., Su, J., Tan, C.L.: Effective adaptation of a hidden Markov model-based named entity recognizer for biomedical domain. In: Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine, vol. 13, pp. 49–56. Association for Computational Linguistics (2003)Google Scholar
  27. 27.
    Simpson, M.S., Demner-Fushman, D.: Biomedical text mining: a survey of recent progress. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 465–517. Springer, Boston (2012).  https://doi.org/10.1007/978-1-4614-3223-4_14CrossRefGoogle Scholar
  28. 28.
    Sondhi, P.: A survey on named entity extraction in the biomedical domain (2008)Google Scholar
  29. 29.
    Takeuchi, K., Collier, N.: Bio-medical entity extraction using support vector machines. Artif. Intell. Med. 33(2), 125–137 (2005)CrossRefGoogle Scholar
  30. 30.
    Tasneem, A., Archana, B.: A survey on biomedical named entity extraction. Asian J. Eng. Technol. Innov. 4(7), 25–28 (2016)Google Scholar
  31. 31.
    Uzuner, Ö., Solti, I., Cadag, E.: Extracting medication information from clinical text. J. Am. Med. Inform. Assoc. 17(5), 514–518 (2010)CrossRefGoogle Scholar
  32. 32.
    Estopà, R., Vivaldi, J., Cabré, M.T.: Use of Greek and Latin forms for term detection. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2000), vol. 78, pp. 855–859 (2000)Google Scholar
  33. 33.
    Weegar, R., Casillas, A., de Ilarraza, A.D., Oronoz, M., Prez, A., Gojenola, K.: The impact of simple feature engineering in multilingual medical NER. In: Proceedings of the Clinical Natural Language Processing Workshop, pp. 1–6 (2016)Google Scholar
  34. 34.
    Xu, H., Stetson, P.D., Friedman, C.: A study of abbreviations in clinical notes. In: AMIA 2007, American Medical Informatics Association Annual Symposium, Chicago, IL, USA (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Viviana Cotik
    • 1
    Email author
  • Horacio Rodríguez
    • 2
  • Jorge Vivaldi
    • 3
  1. 1.Department of Computer Science, FCEyNUniversidad de Buenos AiresBuenos AiresArgentina
  2. 2.Polytechnical University of CataloniaBarcelonaSpain
  3. 3.Universitat Pompeu FabraBarcelonaSpain

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