To characterize and ameliorate the weaknesses of clinical negation detection techniques across corpora that have different annotation schema.
Negation's Not Solved: Reconsidering Negation Annotation and Evaluation
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Though negation detection is a straightforward task in relatively constrained settings, when evaluated in heterogeneous corpora and annotation schema, performance may fall well below that of published benchmarks for both machine learning-based and rule-based approaches. Furthermore, it is difficult to determine the optimal mix of training data, or a standardized way to constrain evaluation metrics, since both are influenced by the corpus and annotation characteristics.