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Fig. 2 | BMC Bioinformatics

Fig. 2

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

Fig. 2

Overview of our model. The Bi-LSTM first encodes each word by concatenating word and position embeddings, followed the multihead attention directly draws the global dependencies of the Bi-LSTM output. Then, sentence embedding is concatenated with relation information, which comes from the KG. edrug,egene and emutation are the drug, gene and mutation entities, respectively. vdruggene and vdrugmutation denote the different relation vectors. Finally, sentence representation with entity relation information is fed to a softmax classifier

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