Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference

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