Joint Drug Entities and Relations Extraction Based on Neural Networks

Drug entities and relations extraction can accelerate biomedical research, and they are also the basis for further building a biomedical knowledge base and other researches. Traditionally, the pipeline method was used to tackle this problem. This method identifies entities in the paper by NER (named entity recognition) firstly, and then handles RC (relation classification) on each entity pair. The pipline method has three problems. The first is error propagation problem. In detail, the wrong NER results will lead to the wrong relation classification results. The remaining two problems are that it ignores the interaction between two subtasks and the interaction between different relations in the sentence. Considering these problems, this article proposes a joint drug entities and relations extraction method based on neural networks. This method employs a new tagging scheme which represents both entity and relation information by the tags and converts the joint extraction task to a tagging problem. This method applies word embedding and character embedding as input, and extracts drug entities and relations with BiLSTM-CRF model. The results shows that, on DDI (drug-drug interactions) 2013 corpus, this method achieves 89.9% F-score for NER and 67.3% F-score for RE (relations extraction) which is better than the pipeline method using the same model.