论文标题
通过因果关系预言的语义依赖性森林支持医疗关系提取
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
论文作者
论文摘要
医疗关系提取（MRE）任务旨在提取医学文本中实体之间的关系。传统的关系提取方法通过探索句法信息，例如依赖树，从而获得了令人印象深刻的成功。但是，由外域解析器产生的医学文本的1好的依赖树的质量相对有限，因此医疗关系提取方法的性能可能会退化。为此，我们提出了一种基于因果解释理论的医学文本中共同模拟语义和句法信息的方法。我们生成依赖性森林，这些森林由1-最佳依赖树组成。然后，采用特定于任务的因果解释者来修剪依赖性森林，该森林将进一步送入设计的图形卷积网络，以学习下游任务的相应表示形式。从经验上讲，基准医学数据集的各种比较证明了我们模型的有效性。
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.
