Papers with graph convolutional networks model

1 papers
Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction (D19-62)

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Challenge: 80% of the data sets for relation extraction tasks are negative instances, resulting in a lack of syntactic information between two entity mentions.
Approach: They propose a graph convolutional networks model that incorporates dependency parsing and contextualized embedding to capture comprehensive contextual information.
Outcome: The proposed model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task.

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