Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

BMC Bioinformatics
Di ZhaoZhihao Yang

Abstract

Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence n-ary relation extraction task, the mainstream method not only relies heavily on syntactic parsing but also ignores prior knowledge. In this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph. Our model is built on self-attention, which can directly capture the relations between two words regardless of their syntactic relation. In addition, our method makes use of entity and relation information from the knowledge base to impose assistance while predicting the relation. Experiments on n-ary relation extraction show that combining context and knowledge representations can significantly improve the n-ary relation extraction performance. Meanwhile, we achieve comparable results with state-of-the-art methods. We explored a novel method for cross-sentence n-ary relation extraction. Unlike previous approaches, our methods operate directly on the sequence and learn how to model the internal structure...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jan 1, 1994·IEEE Transactions on Neural Networks·Y BengioP Frasconi
Feb 7, 2015·Cancer Discovery·Rodrigo DienstmannJustin Guinney
Jul 12, 2018·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Huiwei ZhouDegen Huang
Apr 10, 2019·BMC Medical Informatics and Decision Making·Di ZhaoChunmei Yang
May 28, 2019·Database : the Journal of Biological Databases and Curation·Yijia ZhangYuanyuan Sun
Sep 27, 2019·Journal of Biomedical Informatics·Yijia ZhangZhehuan Zhao

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TransE
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