NEGATIONS NOT SOLVED: GENERALIZABILITY VERSUS OPTIMIZABILITY IN CLINICAL NATURAL LANGUAGE PROCESSING
MITRE CORP BEDFORD MA BEDFORD United States
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A review of published work in clinical natural language processing NLP may suggest that the negation detection task has been solved. This work contends that an optimizable solution does not equal a generalizable solution. Using four manually annotated corpora of clinical text, we show that negation detection can be optimized in relatively constrained settings, but performance is not reliably generalizable unless in-domain training data is available in which case fully supervised domain adaptation techniques may prove effective. Various factors e.g., annotation guidelines, named entity characteristics, the amount of data, and lexical and syntactic context play a role in making generalizability difficult, but none completely explains the phenomenon. This indicates the need for future work in domain-adaptive and task-adaptive methods for clinical NLP.