论文标题
开放研究知识图中的基于SCIBERT的生物测定语素
SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph
论文作者
论文摘要
作为对生物学分析的语义问题的新贡献，在本文中，我们提出了一种基于神经网络的方法，以自动使用Semantify，从而结构结构，非结构化的生物测定文本描述。为此，实验评估显示出有望，因为基于神经的语素显着超过了基于天真的频率基线方法。具体而言，神经方法从基于频率的方法中获得72％的F1，而47％F1。
As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequency-based baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method.
