论文标题
PHICON：通过数据增强改善临床文本去识别模型的概括
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation
论文作者
论文摘要
去识别是在临床文本中识别受保护的健康信息（PHI）的任务。现有的神经去识别模型通常无法推广到新的数据集。我们提出了一种简单而有效的数据增强方法Phicon来减轻概括问题。 Phicon由Phi扩展和上下文增强组成，该增强和上下文扩展是通过用从外部来源采样的指定原则替换PHI实体，并分别用同义词替换或随机单词插入来创建增强的培训语料库。 I2B2 2006和2014 DE-INDICE挑战数据集的实验结果表明，Phicon可以帮助三种选定的去识别模型在交叉数据库测试设置上提高F1分数（最多为8.6％）。我们还讨论了要使用多少增强以及每种增强方法如何影响性能。
De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6%) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance.
