Search Results for author:
Found 8 papers, 2 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.
Knowledge-aware Attention Network for Protein-Protein Interaction Extraction
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts.
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts.
Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge
KBs are important resources for biomedical relation extraction.
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction.
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
Results: This paper proposes a novel model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior knowledge for CDR extraction.
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.
