Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022)

Abstract: Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate. To this end, we propose a method to jointly model semantic and syntactic information from medical texts based on causal explanation theory. We generate dependency forests consisting of the semantic-embedded 1-best dependency tree. Then, a task-specific causal explainer is adopted to prune the dependency forests, which are further fed into a designed graph convolutional network to learn the corresponding representation for downstream task. Empirically, the various comparisons on benchmark medical datasets demonstrate the effectiveness of our model.

## introduction medical relation extraction (mre) refers to identifying relations among entities from medical literature and reports. it plays a very important role in downstream tasks such as medical knowledge graph construction @xcite and biomedical knowledge discovery @xcite . on the other hand, as the number of medical literature increases, it becomes increasingly important to automatically discover the relation among entities in the literature @xcite . the addition of syntactic structure has been demonstrated to be beneficial for various natural language processing tasks @xcite @xcite . as a type of syntactic structure, the dependency tree capturing long-distance connections between words can indeed improve benchmark relation extraction methods @xcite @xcite ). we demonstrate an example in figure 1 . specifically, the 1-best dependency tree of the sentence "aminopropylindenes derived from grundmann's ketone as a novel chemotype of oxidosqualene cyclase inhibitors" in the cpr dataset. aminopropylindenes and oxidosqualene cyclase are the entities, and the relation between them is "down regulator", denoted as "cpr:4". however, in the medical field, the quality of the 1-best dependency tree generated by the out-ofdomain parsers, e.g., parsers for the news domain, is relatively deficient. generally, the main verb in a sentence is treated as the root node in the dependency tree, while, as the example shown in figure 1 , the entity aminopropylindenes, apparently a noun, is treated as the root node. to solve this problem, multiple methods with dependency forests have been proposed @xcite @xcite . such approaches focus on redesigning the parser or substituting the parser with a semantic encoder, but the semantic and dependency tree syntactic information is used in a biased manner. furthermore, the causality between the edges in the dependency forests and the performance of the model is not explored by benchmark methods. to this end, we propose a novel approach, namely causality-pruned semantic dependency forest graph convolutional network (cp-gcn). to acquire the dependency forests enriched with semantic and syntactic information in an unbiased manner, we first obtain the 1-best dependency tree, as the sentence syntactic information, which is generated by the out-of-domain parser, and then fuse the syntactic information with the semantic information by using a switch gate network. the semantic information is captured in different representation subspaces using multi-head attention @xcite . to extract dependency forests' edges that are causally related to the mre performance, we construct a causal explanation dataset based on granger causality @xcite and train a task-specific causal explainer. we then obtain taskspecific explanations of the dependency forests generated by the trained explainer and prune the dependency forests by following the corresponding explanations, which aim to eliminate the taskirrelevant information from the dependency forests. the pruned dependency forests are encoded by dcgcns @xcite for mre task. empirically, the comparisons demonstrate that cp-gcn achieves state-of-the-art on benchmark relation extraction tasks, e.g., for the sentence-level relation extraction task, our model obtains 67.3 and 92.9 scores on cpr and pgr, respectively. the contributions are summarized as follows: ## methodology in this section, we introduce our proposed cp-gcn model shown in figure 2 . ## conclusion in this paper, we introduce a novel approach for the medical relation extraction task, namely cp-gcn, which proposes a causality-pruned dependency forest enriched with semantic and syntactic information. we first construct dependency forests by incorporating semantic information into the dependency tree generated by the off-the-shelf parser. then, a task-specific causal explainer is trained to prune the dependency forests. experiments on the benchmark medical datasets demonstrate the superiority of cp-gcn over the state-of-the-art methods for the medical relation extraction task.