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.
@article{arxiv.2012.08790,
title = {Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference},
author = {Yichao Zhou and Yu Yan and Rujun Han and J. Harry Caufield and Kai-Wei Chang and Yizhou Sun and Peipei Ping and Wei Wang},
journal= {arXiv preprint arXiv:2012.08790},
year = {2020}
}
Comments
10 pages, 4 figures, 7 tables, accepted by AAAI 2021