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A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature.
Journal of Biomedical informatics ( IF 4.5 ) Pub Date : 2020-02-04 , DOI: 10.1016/j.jbi.2020.103384
Ling Luo 1 , Zhihao Yang 1 , Mingyu Cao 1 , Lei Wang 2 , Yin Zhang 2 , Hongfei Lin 1
Affiliation  

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.

中文翻译:

用于生物医学实体和从生物医学文献中提取关系的基于神经网络的联合学习方法。

近来,实体和关系的联合建模方法在一般领域比传统的流水线方法显示出更多有希望的结果。但是,由于生物医学文本中的许多重叠关系,它们不适用于生物医学领域。为了缓解该问题,我们提出了一种基于神经网络的联合学习方法,用于生物医学实体和关系提取。在这种方法中,提出了一种考虑重叠关系的新颖标记方案。然后,建立Att-BiLSTM-CRF模型以共同提取实体及其与我们提取规则的关系。此外,在生物医学文本上预先训练的上下文化ELMo表示用于进一步改善性能。
更新日期:2020-02-04
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