A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature.

Recently joint modeling methods of entity and relation exhibit more promising results than traditional pipelined methods in general domain. However, they are inappropriate for the biomedical domain due to numerous overlapping relations in biomedical text. To alleviate the problem, we propose a neural network-based joint learning approach for biomedical entity and relation extraction. In this approach, a novel tagging scheme that takes into account overlapping relations is proposed. Then the Att-BiLSTM-CRF model is built to jointly extract the entities and their relations with our extraction rules. Moreover, the contextualized ELMo representations pre-trained on biomedical text are used to further improve the performance. Experimental results on biomedical corpora show that our method can significantly improve the performance of overlapping relation extraction and achieves the state-of-the-art performance.