Published on in Vol 3, No 3 (2015): Jul-Sep

Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care

Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care

Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care

Journals

  1. Sarker A, Gonzalez-Hernandez G. An unsupervised and customizable misspelling generator for mining noisy health-related text sources. Journal of Biomedical Informatics 2018;88:98 View
  2. Dirkson , Verberne , Sarker , Kraaij . Data-Driven Lexical Normalization for Medical Social Media. Multimodal Technologies and Interaction 2019;3(3):60 View
  3. Yazdani A, Ghazisaeedi M, Ahmadinejad N, Giti M, Amjadi H, Nahvijou A. Automated Misspelling Detection and Correction in Persian Clinical Text. Journal of Digital Imaging 2020;33(3):555 View
  4. Sarker A, Jonathan W. LexExp: a system for automatically expanding concept lexicons for noisy biomedical texts. Bioinformatics 2021;37(16):2499 View
  5. Kim T, Han S, Kang M, Lee S, Kim J, Joo H, Sohn J. Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports. JMIR Medical Informatics 2021;9(2):e25530 View
  6. Hernández J, Molina F, Almela Á. Analysis of Context-Dependent Errors in the Medical Domain in Spanish: A Corpus-Based Study. SAGE Open 2023;13(1):215824402211484 View
  7. Tetzlaff L, Heinrich A, Schadewitz R, Thomeczek C, Schrader T. Die Analyse des CIRSmedical.de mittels Natural Language Processing. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 2022;169:1 View

Books/Policy Documents

  1. López-Hernández J, Almela Á, Valencia-García R. Technologies and Innovation. View