Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

TL;DR

This paper introduces a neural network model that combines document context and prior knowledge from knowledge bases to improve the extraction of chemical-disease relationships, showing significant performance gains.

Contribution

It proposes a novel attention-based neural network that effectively integrates context and knowledge representations for chemical-disease relation extraction.

Findings

Significant improvement in CDR extraction performance.

Achieved results comparable to state-of-the-art methods.

Effective use of prior knowledge enhances relation extraction accuracy.

Abstract

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.