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 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. However, existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG), a novel method 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.

Authors

• Yichao Zhou Nanjing University of Science and Technology
• Yu Yan
• Rujun Han
• J. Harry Caufield
• Kai-Wei Chang
• Yizhou Sun University of California, Los Angeles
• Peipei Ping
• Wei Wang

Keywords

No keywords are indexed for this paper.