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

By Yichao Zhou, Yu Yan, Rujun Han, J. Caufield, Kai-Wei Chang, Yizhou Sun, P. Ping. 2020

55 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nanyun Peng
2. Xiang Ren
3. Rujun Han
4. Shuaicheng Zhang
5. Lifu Huang
6. Qiang Ning
7. Dan Roth
8. Yejin Choi

In Molecular Biology

1. Xiang Ren
2. Nanyun Peng
3. Qiang Ning
4. Rujun Han

Citations in the corpus

• ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “Recent approaches leveraging large pre-trained language models (PTLMs) achieved state-of-the-art results on a range of event temporal reasoning tasks (Ning et al., 2020; Pereira et al., 2020; Wang et al., 2020; Zhou et al., 2020c; Han et al., 2019b).”
• Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer (2021, background): “Recently, deep neural networks (Dligach et al., 2017; Tourille et al., 2017) and large-scale pre-training language models (Han et al., 2019a, 2020; Wang et al., 2020a; Zhou et al., 2020) are further employed and show state-of-the-art performance on temporal relation extraction.”