This paper is in the following e-collection/theme issue:

Tools, Programs and Algorithms (177) Natural Language Processing (470) Electronic Health Records (747)

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

Authors of this article:

Xiaofang Zhou 1, 2 Author Orcid Image ;   An Zheng 2 Author Orcid Image ;   Jiaheng Yin 2, 3 Author Orcid Image ;   Rudan Chen 2 Author Orcid Image ;   Xianyang Zhao 2 Author Orcid Image ;   Wei Xu 2 Author Orcid Image ;   Wenqing Cheng 2 Author Orcid Image ;   Tian Xia 2, 4 Author Orcid Image ;   Simon Lin 5 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

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 2020 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

Books/Policy Documents

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