A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction
This addresses the challenge of overlapping triples in medical relation extraction, which is incremental as it builds on existing tagging methods.
The paper tackles the problem of overlapping triples in joint medical relation extraction by proposing a Bidirectional Tree Tagging (BiTT) scheme that forms triples into binary trees and converts them into tags, resulting in a model that outperforms baselines by 2.0% and 2.5% in F1 score on medical datasets.
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.