Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
This work addresses the problem of extracting chemical-disease relations for biomedical research and healthcare, but it is incremental as it builds on existing methods by incorporating knowledge bases.
The paper tackled chemical-disease relation extraction by proposing a neural attention model that combines context from documents with prior knowledge from knowledge bases, achieving significant performance improvements on the BioCreative V CDR dataset with results comparable to state-of-the-art systems.
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.