title: Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care.
abstract: BACKGROUND
  Consumer-generated content, such as postings on social media websites, can serve as an ideal source of information for studying health care from a consumer's perspective. However, consumer-generated content on health care topics often contains spelling errors, which, if not corrected, will be obstacles for downstream computer-based text analysis.
  
  
  OBJECTIVE
  In this study, we proposed a framework with a spelling correction system designed for consumer-generated content and a novel ontology-based evaluation system which was used to efficiently assess the correction quality. Additionally, we emphasized the importance of context sensitivity in the correction process, and demonstrated why correction methods designed for electronic medical records (EMRs) failed to perform well with consumer-generated content.
  
  
  METHODS
  First, we developed our spelling correction system based on Google Spell Checker. The system processed postings acquired from MedHelp, a biomedical bulletin board system (BBS), and saved misspelled words (eg, sertaline) and corresponding corrected words (eg, sertraline) into two separate sets. Second, to reduce the number of words needing manual examination in the evaluation process, we respectively matched the words in the two sets with terms in two biomedical ontologies: RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT). The ratio of words which could be matched and appropriately corrected was used to evaluate the correction system's overall performance. Third, we categorized the misspelled words according to the types of spelling errors. Finally, we calculated the ratio of abbreviations in the postings, which remarkably differed between EMRs and consumer-generated content and could largely influence the overall performance of spelling checkers.
  
  
  RESULTS
  An uncorrected word and the corresponding corrected word was called a spelling pair, and the two words in the spelling pair were its members. In our study, there were 271 spelling pairs detected, among which 58 (21.4%) pairs had one or two members matched in the selected ontologies. The ratio of appropriate correction in the 271 overall spelling errors was 85.2% (231/271). The ratio of that in the 58 spelling pairs was 86% (50/58), close to the overall ratio. We also found that linguistic errors took up 31.4% (85/271) of all errors detected, and only 0.98% (210/21,358) of words in the postings were abbreviations, which was much lower than the ratio in the EMRs (33.6%).
  
  
  CONCLUSIONS
  We conclude that our system can accurately correct spelling errors in consumer-generated content. Context sensitivity is indispensable in the correction process. Additionally, it can be confirmed that consumer-generated content differs from EMRs in that consumers seldom use abbreviations. Also, the evaluation method, taking advantage of biomedical ontology, can effectively estimate the accuracy of the correction system and reduce manual examination time.
authors.affiliation: The Department of Ophthalmology, Wuhan Central Hospital, Wuhan, China.
authors.forename: Xiaofang
authors.identifier: http://orcid.org/0000-0003-1155-364X
authors.initials: X
authors.lastname: Zhou
authors.forename: An
authors.identifier: http://orcid.org/0000-0002-3689-2712
authors.initials: A
authors.lastname: Zheng
authors.forename: Jiaheng
authors.identifier: http://orcid.org/0000-0002-5567-2301
authors.initials: J
authors.lastname: Yin
authors.forename: Rudan
authors.identifier: http://orcid.org/0000-0002-3293-687X
authors.initials: R
authors.lastname: Chen
authors.forename: Xianyang
authors.identifier: http://orcid.org/0000-0003-2380-8099
authors.initials: X
authors.lastname: Zhao
authors.forename: Wei
authors.identifier: http://orcid.org/0000-0001-5174-8543
authors.initials: W
authors.lastname: Xu
authors.forename: Wenqing
authors.identifier: http://orcid.org/0000-0001-7840-1195
authors.initials: W
authors.lastname: Cheng
authors.forename: Tian
authors.identifier: http://orcid.org/0000-0003-1721-6126
authors.initials: T
authors.lastname: Xia
authors.forename: Simon
authors.identifier: http://orcid.org/0000-0003-2876-2042
authors.initials: S
authors.lastname: Lin
keywords: biomedical ontology; consumer-generated content; context sensitive; spelling correction system