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arXiv 2012.087902020-12-16cs.CLcs.LG

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

University of California, Los Angeles（加州大学洛杉矶分校）；University of Southern California（南加州大学）

Yichao Zhou, Yu Yan, Rujun Han, J. Harry Caufield, Kai-Wei Chang, Yizhou Sun, Peipei Ping, Wei Wang

备注：: 10 pages, 4 figures, 7 tables, accepted by AAAI 2021

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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.

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