Linguistic errors in the biomedical domainTowards an error typology for Spanish

  1. Jésica López Hernández
  2. Rafael Valencia-García
  3. Ángela Almela
Revista:
Sintagma: Revista de lingüística

ISSN: 0214-9141

Año de publicación: 2021

Volumen: 33

Páginas: 83-100

Tipo: Artículo

Otras publicaciones en: Sintagma: Revista de lingüística

Referencias bibliográficas

  • Aguilar Ruiz, M. J. (2013). Las normas ortográficas y ortotipográficas de la nueva Ortografía de la lengua española (2010) aplicadas a las publicaciones biomédicas en español: una visión de conjunto. Panace@, 14(37), 101-120.
  • Ahmed, F., Luca, E. W. D., & Nurnberger, A. (2009). Revised N-Gram based automatic spelling correction tool to improve retrieval effectiveness. Polibits, 39-48.
  • Baba, Y., & Suzuki, H. (2012). How are spelling errors generated and corrected? A study of corrected and uncorrected spelling errors using keystroke logs. In H. Li, C. Lin, M. Osborne, G. G. Lee & J. C. Park (Eds.), Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers (pp. 373-377). Jeju Island: Association for Computational Linguistics (ACL).
  • Cantos, P. (2013). Statistical Methods in Language and Linguistic Research. Sheffield, UK: Equinox Publishing.
  • Cantos, P., & Almela, A (2019). Readability indices for the assessment of textbooks: a feasibility study in the context of EFL. Vigo International Journal of Applied Linguistics, 16, 31-52.
  • Corder, S. P. (1967). The Significance of Learners’ Errors. International Review of Applied Linguistics in Language Teaching, 5, 161-170.
  • Damerau, F. J. (1964). A Technique for Computer Detection and Correction of Spelling Errors. Communications of ACM, 7(3), 171-177.
  • Díaz Villa, A. (2005). Tipología de errores gramaticales para un corrector automático. Procesamiento del Lenguaje Natural, 35, 409-416.
  • Fivez P., Suster, S., & Daelemans, W. (2016). Unsupervised context-sensitive spelling correction of clinical free-text with word and character N-Gram embeddings. In K. B. Cohen, D. Demner-Fushman, S. Ananiadou & J. Tsujii (Eds.), Proceedings of the BioNLP 2017 Workshop, (pp. 143-148). Vancouver: Association for Computational Linguistics (ACL).
  • García-Díaz, J. A., Cánovas-García, M., & Valencia-García, R. (2020). Ontologydriven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America. Future Generation Computer Systems, 112, 641-657. doi:10.1016/j.future.2020.06.019.
  • Gimenes, P. A., Roman, N. T., & Carvalho, A. M. (2015). Spelling Error Patterns in Brazilian Portuguese. Computational Linguistics, 41(1), 175-183. doi:10.1162/coli_a_00216.
  • Gutiérrez Rodilla, B. (2005). El lenguaje de las ciencias. Madrid: Gredos.
  • Harremoës, P., & Topsøe, F. (2005). Zipf’s law, hyperbolic distributions and entropy loss. Electronic Notes in Discrete Mathematics, 21, 315-318. doi:10.1109/ISIT.2002.1023479.
  • Kilicoglu, H., Fiszman, M., Roberts, K., & Demner-Fushman, D. (2015). An ensemble method for spelling correction in consumer health questions. In American Medical Informatics Association (Eds.), AMIA Annual Symposium Proceedings (pp. 727-736). San Francisco: AMIA.
  • Kukich, K. (1992). Technique for automatically correcting words in text. ACM Computing Surveys, 24(4), 377-439. doi:10.1145/146370.146380
  • Lehal, G. S., & Bhagat, M. (2007). Spelling error pattern analysis of Punjabi typed text. In Proceedings of the 2007 International Symposium on Machine Translation, NLP and TSS (pp. 128-141). New Delhi: Tata McGraw-Hill.
  • Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl., 707-710.
  • López-Hernández J., Almela Á., & Valencia-García R. (2019). Automatic Spelling Detection and Correction in the Medical Domain: A Systematic Literature Review. In R. Valencia-García, G. Alcaraz-Mármol, J. Del Cioppo-Morstadt, N. VeraLucio & M. Bucaram-Leverone (Eds.) Technologies and Innovation. CITI 2019. Communications in Computer and Information Science (vol. 1124, pp. 104-117). Cham: Springer.
  • Meystre, S., & Haug, P. (2006). Natural language processing to extract medical problems from electronic clinical documents: Performance evaluation. Journal of Biomedical Informatics, 39, 589-599.
  • Mitton, R. (1987). Spelling checkers, spelling correctors, and the misspellings of poor spellers. Information Processing & Management, 23(5): 495-505.
  • Naber, D. (2003). A rule-based style and grammar checker. Munich: GRIN Verlag.
  • Nagata, R., Takamura, H., & Neubig, G. (2017). Adaptive spelling error correction models for learner English. Procedia Computer Science, 112, 474-483. doi:10.1016/j. procs.2017.08.065
  • Paggio, P. (2000). Spelling and grammar correction for Danish in SCARRIE. In Association for Computational Linguistics (Eds.), Proceedings of the Sixth Conference on Applied Natural Language Processing, (pp. 255-261). Seattle.
  • Patrick, J., Sabbagh, M., Jain, S., & Zheng, H. (2010). Spelling correction in clinical notes with emphasis on first suggestion accuracy. In Second Workshop on Building and Evaluating Resources for Biomedical Text Mining (pp. 2-8). Malta: Association for Natural Language Processing.
  • Pollock, J. J., & Zamora, A. (1983). Collection and characterization of spelling errors in scientific and scholarly text. Journal of American Society of Informatics and Science, 34(1), 51-58.
  • Rambell, O. (1999). Error typology for automatic proof-reading purposes. In A. Sagvall Hein (Ed.), Reports from the SCARRIE project (pp. 1-29). Uppsala: Uppsala University.
  • Ramírez, F., & López, E. (2006). Spelling Error Patterns in Spanish for Word Processing Applications, In Proceedings of Fifth international conference on Language Resources and Evaluation LREC’06 (pp. 93-98). Genoa: European Language Resources Association.
  • Real Academia Española y Asociación de Academias de la Lengua Española. (2010). Ortografía de la lengua española. Madrid: Espasa.
  • Real Academia Nacional de Medicina. (2012). Diccionario de Términos Médicos. Madrid: Panamericana.
  • Ruch, B., & Geissbühler, A. (2003). Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record. Artificial intelligence in medicine, 29(2), 169-84. doi:10.1016/s0933-3657(03)00052-6.
  • Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589-1604. doi:10.1109/jbhi.2017.2767063.
  • Senger, C., Kaltschmidt, J., Schmitt, S. P. W., Pruszydlo, M. G., & Haefeli, W. E. (2010). Misspellings in drug information system queries: characteristics of drug name spelling errors and strategies for their prevention. International Journal of Medical Informatics, 79(12), 832–839. doi: 10.1016/j.ijmedinf.2010.09.005.
  • Siklósi, B., Novák, A., & Prószéky, G. (2016). Context-aware correction of spelling errors in Hungarian medical documents. Computer Speech & Language, 35, 219-233.
  • Veronis, J. (1988). Computerized correction of phonographic errors. Computers and the Humanities, 22(1), 43-56.
  • Wong, W., & Glance, D. (2011). Statistical semantic and clinician confidence analysis for correcting abbreviations and spelling errors in clinical progress notes. Artificial Intelligence in Medicine, 53(3), 171-180.
  • Yannakoudakis, E. J., & Fawthrop, D. (1983). The rules of spelling errors. Information processing and management, 19(12), 101-108.