Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

• URL: http://arxiv.org/abs/2208.13472v1
• Date: Mon, 29 Aug 2022 10:17:47 GMT
• Title: Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
• Authors: Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, Xiaohui Hu
• Abstract summary: We propose a method to 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. A task-specific causal explainer is adopted to prune the dependency forests, which are then fed into a designed graph convolutional network to learn the corresponding representation.
• Score: 8.73855139557187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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