Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks
Journal:
arXiv
Published Date:
Jan 2, 2025
Abstract
Many models are proposed in the literature on biomedical event
extraction(BEE). Some of them use the shortest dependency path(SDP) information
to represent the argument classification task. There is an issue with this
representation since even missing one word from the dependency parsing graph
may totally change the final prediction. To this end, the full adjacency matrix
of the dependency graph is used to embed individual tokens using a graph
convolutional network(GCN). An ablation study is also done to show the effect
of the dependency graph on the overall performance. The results show a
significant improvement when dependency graph information is used. The proposed
model slightly outperforms state-of-the-art models on BEE over different
datasets.
