Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2020 | EKAW | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Anteghini | 1 | 1 | 1.39 |
| Jennifer D'Souza | 2 | 1 | 2.05 |
| Vítor A. P. Martins dos Santos | 3 | 0 | 0.34 |
| Sören Auer | 4 | 5711 | 418.56 |