  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.
