To minimize the accelerating amount of time invested in the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves overall the baselines (deep learning techniques) and single-task models for predicting the relationships, without compromising on the performance of all the tasks.

中文翻译：

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从生物医学和临床文本中提取关系：统一多任务学习框架

为了最大限度地减少在生物医学文献搜索中投入的加速时间，已经提出了许多自动知识提取的方法。关系抽取就是这样一项任务，其中实体之间的语义关系是从自由文本中识别出来的。在生物医学领域，提取调控途径、代谢过程、药物不良反应或疾病模型需要从个体关系中获得知识，例如基因、蛋白质、药物、化学、疾病或表型之间的物理或调控相互作用。在本文中，我们从三个主要的生物医学和临床任务中研究关系提取任务，即药物-药物相互作用、蛋白质-蛋白质相互作用和医学概念关系提取。对此，我们在多任务学习 (MTL) 框架中对关系提取问题进行建模，并首次引入了结构化自我注意网络的概念，并辅以对抗性学习方法，用于预测生物医学和临床文本中的关系。MTL 的基本概念是通过利用共享表示的概念同时学习多个问题。此外，我们还生成了高效的单任务模型，该模型利用在注意门控循环单元上学习的最短依赖路径嵌入来比较我们提出的 MTL 模型。我们提出的框架显着改善了用于预测关系的基线（深度学习技术）和单任务模型的整体性能，而不会影响所有任务的性能。此外，我们还生成了高效的单任务模型，该模型利用在注意门控循环单元上学习的最短依赖路径嵌入来比较我们提出的 MTL 模型。我们提出的框架显着改善了用于预测关系的基线（深度学习技术）和单任务模型的整体性能，而不会影响所有任务的性能。此外，我们还生成了高效的单任务模型，该模型利用在注意门控循环单元上学习的最短依赖路径嵌入来比较我们提出的 MTL 模型。我们提出的框架显着改善了用于预测关系的基线（深度学习技术）和单任务模型的整体性能，而不会影响所有任务的性能。