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An unsupervised and customizable misspelling generator for mining noisy health-related text sources
BACKGROUND: Data collection and extraction from noisy text sources such as social media typically rely on keyword-based searching/listening. However, health-related terms are often misspelled in such noisy text sources due to their complex morphology, resulting in the exclusion of relevant data for...
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| Veröffentlicht in: | J Biomed Inform |
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| Hauptverfasser: | , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
2018
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6322919/ https://ncbi.nlm.nih.gov/pubmed/30445220 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jbi.2018.11.007 |
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