论文标题
与概率软逻辑正则化和全局推断的临床时间关系提取
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
论文作者
论文摘要
医学界一直始终需要精确提取临床事件之间的时间关系。特别是，时间信息可以促进各种下游应用程序，例如案例报告检索和医疗问题回答。现有方法要么需要昂贵的功能工程，要么无法对事件之间的全球关系依赖性进行建模。在本文中，我们提出了一种新颖的方法，即具有概率软逻辑正则化和全球推理（CTRL-PG）的临床时间关系外观，以在文档级别解决该问题。在两个基准数据集I2B2-2012和TB致密上进行的广泛实验表明，CTRL-PG显着胜过时间关系提取的基线方法。
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.
