论文标题
使用深层实体和关系模型揭示电晕病毒图
Uncovering the Corona Virus Map Using Deep Entities and Relationship Models
论文作者
论文摘要
我们通过采用新型实体和关系模型来从与Corona病毒有关的文章中提取与Covid-19相关的实体和关系。实体识别和关系发现模型通过大型注释语料库的多任务学习目标进行了培训。我们采用概念掩盖范式来防止神经网络的演变，该神经网络充当关联记忆，并引起正确的归纳偏见，从而指导网络仅使用上下文进行推理。我们发现了几个进口子网，突出了重要的术语和概念，并阐明了过去相关疾病中使用的几种治疗方式。
We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus by employing a novel entities and relationship model. The entity recognition and relationship discovery models are trained with a multi-task learning objective on a large annotated corpus. We employ a concept masking paradigm to prevent the evolution of neural networks functioning as an associative memory and induce right inductive bias guiding the network to make inference using only the context. We uncover several import subnetworks, highlight important terms and concepts and elucidate several treatment modalities employed in related ailments in the past.
