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Joint medical relation extraction refers to extracting triples, composed ofentities and relations, from the medical text with a single model. One of thesolutions is to convert this task into a sequential tagging task. However, inthe existing works, the methods of representing and tagging the triples in alinear way failed to the overlapping triples, and the methods of organizing thetriples as a graph faced the challenge of large computational effort. In thispaper, inspired by the tree-like relation structures in the medical text, wepropose a novel scheme called Bidirectional Tree Tagging (BiTT) to form themedical relation triples into two two binary trees and convert the trees into aword-level tags sequence. Based on BiTT scheme, we develop a joint relationextraction model to predict the BiTT tags and further extract medical triplesefficiently. Our model outperforms the best baselines by 2.0\% and 2.5\% in F1score on two medical datasets. What's more, the models with our BiTT schemealso obtain promising results in three public datasets of other domains.

A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction