  De-identification is the task of identifying protected health information
(PHI) in the clinical text. Existing neural de-identification models often fail
to generalize to a new dataset. We propose a simple yet effective data
augmentation method PHICON to alleviate the generalization issue. PHICON
consists of PHI augmentation and Context augmentation, which creates augmented
training corpora by replacing PHI entities with named-entities sampled from
external sources, and by changing background context with synonym replacement
or random word insertion, respectively. Experimental results on the i2b2 2006
and 2014 de-identification challenge datasets show that PHICON can help three
selected de-identification models boost F1-score (by at most 8.6%) on
cross-dataset test setting. We also discuss how much augmentation to use and
how each augmentation method influences the performance.
