论文标题
联合医疗关系提取的双向树标记方案
A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction
论文作者
论文摘要
联合医疗关系提取是指由单个模型从医学文本中提取由实体和关系组成的三元组。解决方案之一是将此任务转换为顺序标记任务。但是，在现有的作品中，以线性方式表示和标记三元组的方法失败了，将三分之一的三元组整理为图表，面临着大量计算工作的挑战。在本文中，受到医学文本中树状的关系结构的启发，我们提出了一个名为“双向树”标签（BITT）的新颖方案，将医疗关系三元组形成两条两二进制树，并将树转换为单词级别的标签序列。基于BITT方案，我们开发了一个联合关系提取模型，以预测BITT标签并进一步提取医疗三元三元。我们的模型在两个医疗数据集上的最佳基准在F1分中的最佳基线分别为2.0 \％。此外，使用我们的BITT方案的模型还可以在其他域的三个公共数据集中获得有希望的结果。
Joint medical relation extraction refers to extracting triples, composed of entities and relations, from the medical text with a single model. One of the solutions is to convert this task into a sequential tagging task. However, in the existing works, the methods of representing and tagging the triples in a linear way failed to the overlapping triples, and the methods of organizing the triples as a graph faced the challenge of large computational effort. In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence. Based on BiTT scheme, we develop a joint relation extraction model to predict the BiTT tags and further extract medical triples efficiently. Our model outperforms the best baselines by 2.0\% and 2.5\% in F1 score on two medical datasets. What's more, the models with our BiTT scheme also obtain promising results in three public datasets of other domains.
