ArticleJournal of the American Medical Informatics Association : JAMIA2019
Spell checker for consumer language (CSpell).
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Generalizing machine learning models from clinical free text.Scientific reports · 2025Article
- Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence.Proceedings of machine learning research · 2022Article
- Application of natural language processing techniques to identify off-label drug usage from various online health communities.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Automatic classification of scanned electronic health record documents.International journal of medical informatics · 2020Article
- The Unified Medical Language System SPECIALIST Lexicon and Lexical Tools: Development and applications.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Consumer health information and question answering: helping consumers find answers to their health-related information needs.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The journey to transparency, reproducibility, and replicability.Journal of the American Medical Informatics Association : JAMIA · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Automated understanding of consumer health inquiries might be hindered by misspellings. To detect and correct various types of spelling errors in consumer health questions, we developed a distributable spell-checking tool, CSpell, that handles nonword errors, real-word errors, word boundary infractions, punctuation errors, and combinations of the above. Methods: We developed a novel approach of using dual embedding within Word2vec for context-dependent corrections. This technique was used in combination with dictionary-based corrections in a 2-stage ranking system. We also developed various splitters and handlers to correct word boundary infractions. All correction approaches are integrated to handle errors in consumer health questions. Results: Our approach achieves an F1 score of 80.93% and 69.17% for spelling error detection and correction, respectively. Discussion: The dual-embedding model shows a significant improvement (9.13%) in F1 score compared with the general practice of using cosine similarity with word vectors in Word2vec for context ranking. Our 2-stage ranking system shows a 4.94% improvement in F1 score compared with the best 1-stage ranking system. Conclusion: CSpell improves over the state of the art and provides near real-time automatic misspelling detection and correction in consumer health questions. The software and the CSpell test set are available at https://umlslex.nlm.nih.gov/cSpell.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.