The architecture of the graph concat model with fixed and trainable GNN weights.

The architecture of the graph concat model with fixed and trainable GNN weights.

<p>The architecture of the graph concat model with fixed and trainable GNN weights.</p>

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Bibliographic Details
Main Author:	Philipp Wegner (20896651) (author)
Other Authors:	Holger Fröhlich (31585) (author), Sumit Madan (6064379) (author)
Published:	2025
Subjects:	Biotechnology Cancer Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified websites like askapatient pharmacovigilance work conducted named entity recognition medical regulatory bodies graph attention networks graph attention network economic interests serve based nlp models based language models text </ p knowledge fusion models knowledge fusion approaches div >< p additional contextual knowledge modality model consisting extract ade mentions identify ade spans knowledge graphs context knowledge model achieved ernie model baseline model ade corpus identify ades various types valuable source textual data tac corpus smm4h corpus psytar corpus pharmaceutical industry motivating factors modal architecture important role extraction task essential part drug labels diverse datasets cadec corpus already available
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