  There has been a steady need in the medical community to precisely extract
the temporal relations between clinical events. In particular, temporal
information can facilitate a variety of downstream applications such as case
report retrieval and medical question answering. Existing methods either
require expensive feature engineering or are incapable of modeling the global
relational dependencies among the events. In this paper, we propose a novel
method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic
Regularization and Global Inference (CTRL-PG) to tackle the problem at the
document level. Extensive experiments on two benchmark datasets, I2B2-2012 and
TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods
for temporal relation extraction.
