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Found 3 papers, 1 papers with code
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction
In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document.
Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection
Finally, the selected relation embedding and the context features are concatenated for PPI extraction.
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks.
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