Log In Sign Up

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

by   Huiwei Zhou, et al.

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.


page 1

page 2

page 3

page 4


Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge

Chemical-disease relation (CDR) extraction is significantly important to...

Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction

Background: Automatic extraction of chemical-disease relations (CDR) fro...

Knowledge-aware Attention Network for Protein-Protein Interaction Extraction

Protein-protein interaction (PPI) extraction from published scientific l...

Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

Protein-protein interaction (PPI) extraction from published scientific l...

BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer

A biomedical relation statement is commonly expressed in multiple senten...

Learning Informative Representations of Biomedical Relations with Latent Variable Models

Extracting biomedical relations from large corpora of scientific documen...

Biomedical Entity Representations with Synonym Marginalization

Biomedical named entities often play important roles in many biomedical ...