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BADREX uses dynamically generated regular expressions to annotate term definition–term abbreviation pairs, and corefers unpaired acronyms and abbreviations back to their initial definition in the text. Against the Medstract corpus BADREX achieves precision and recall of 98% and 97%, and against a much larger corpus, 90% and 85%, respectively. BADREX yields(More)
We describe a method for automating the detection and correction of spelling errors in the Foundational Model of Anatomy (FMA). The FMA was tokenized into 4893 distinct words; misspellings were identified and corrected using the National Library of Medicine's SPECIALIST GSpell Spelling Suggestion API. We identified 43 errors occurring in 97 terms, and 6(More)
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