  The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing
focuses on the de-identification of psychiatric evaluation records. This paper
describes two participating systems of our team, based on conditional random
fields (CRFs) and long short-term memory networks (LSTMs). A pre-processing
module was introduced for sentence detection and tokenization before
de-identification. For CRFs, manually extracted rich features were utilized to
train the model. For LSTMs, a character-level bi-directional LSTM network was
applied to represent tokens and classify tags for each token, following which a
decoding layer was stacked to decode the most probable protected health
information (PHI) terms. The LSTM-based system attained an i2b2 strict
micro-F_1 measure of 89.86%, which was higher than that of the CRF-based
system.
