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Table 3 Average test accuracy in five-fold cross validation of the proposed model and state-of-the-art methods on cross-sentence n-ary relation extraction. “-” denotes that the value is not provided herein. Full Parametrization (FULL) denote as each edge label is associated with a 2D weight matrix to be tuned in training. Type Embedding (EMBED) denote as each edge label to an embedding vector. K in the GCN models means that the preprocessed pruned trees include tokens up to distance K away from the dependency path in the lowest common ancestor subtree. *: significant at p<0.005

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

Method Ternary Binary
  Single Cross Single Cross
Feature-based [31] 74.7 77.7 73.9 75.2
LSTM-CNN [13] 79.6 82.9 85.8 88.5
Graph LSTM-EMBED [5] 76.5 80.6 74.3 76.5
Graph LSTM-FULL [5] 77.9 80.7 75.6 76.7
Graph LSTM MULTITASK [5] - 82.0 - 78.5
GS GLSTM [12] 82.3 85.5 85.4 85.6
GCN (K=0) [15] 85.6 85.8 82.8 82.7
AGGCN [16] 87.1 87.0 85.2 85.6
Bi-LSTM 80.8 85.9 88.6 89.3
GNN 83.0 86.6 88.7 88.6
Multihead attention 81.5 87.1 89.7* 90.6*
With KG 87.3* 91.9* - -