## 5 Results
### Main Result on SemEval and ReLocaR  
On SemEval and ReLocaR, this approach compares with the feature-based model, deep neural network model and pre-trained language model. Table 1 reports the results. Three self-constructed models are compared in the experiment: Entity BERT (BERT model integrating the entity information by joint representation but discarding the syntactic constraints), EBGCN (Entity BERT with GCN, apply normal GCN without attention to impose hard syntactic constraints on Entity BERT), EBAGCN (Entity BERT with Attention-guided GCN, apply attention-guided GCN to impose soft syntactic constraints on Entity BERT). The result in Table 1 shows that the models in this work significantly outperform previous SOTA model SVM+Wikipedia which is based on feature engineering. They also surpass all the DNN models including LSTM, BiLSTM, and PreWin, even if they incorporate POS and NER features to enrich the representation. This result illustrates that the conventional features cannot provide enough contextual information. Entity BERT and BERT are both pre-trained language models. However, there are significant differences in \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**MODEL**} & \multicolumn{3}{c}{**SemEval**} & \multicolumn{3}{c}{**ReLocaR**} \\  & **F1-L** & **F1-M** & **Acc(std)** & **F1-L** & **F1-M** & **Acc(std)** \\ \hline SVM+Wikipedia [19] & 91.6 & 59.1 & 86.2(N/A) & - & - & - \\ \hline LSTM (GLoVE) [43] & 85.2 & 28.7 & 72.6(1.48) & 78.4 & 78.4 & 78.4(0.91) \\ +NER+POS & 87.5 & 27.3 & 77.4(1.34) & 80.6 & 80.6 & 80.6(0.92) \\ \hline BiLSTM (GLoVE) [43] & 83.2 & 37.4 & 75.4(1.72) & 82.9 & 83.0 & 82.9(0.85) \\ +NER+POS & 88.8 & 37.7 & 82.0(1.36) & 84.2 & 84.2 & 84.2(0.69) \\ \hline Paragraph [8] & - & - & 81.3(0.88) & - & - & 80.0(2.25) \\ Immediate 5 [8] & - & - & 81.3(1.11) & - & - & 81.4(1.34) \\ Immediate 10 [8] & - & - & 81.9(0.89) & - & - & 81.3(1.44) \\ PreWin [8] & 90.6 & 57.3 & 83.1(0.64) & 84.4 & 84.8 & 83.6(0.71) \\ \hline BiLSTM (ELMo) [43] & 91.9 & 54.7 & 86.3(0.45) & 90.0 & 90.1 & 90.0(0.40) \\ +NER+POS & 91.6 & 55.6 & 86.1(0.47) & 90.1 & 90.1 & 90.1(0.36) \\ \hline BERT [16] & - & - & 84.7(0.71) & - & - & 91.3(0.54) \\ \hline Entity BERT (this work) & 93.2 & 66.0 & 88.8(0.63) & 95.2 & 95.3 & 95.3(0.44) \\ EBGCN (this work) & 93.5 & 67.5 & 89.1(0.60) & 95.5 & 95.5 & 95.5(0.46) \\ EBAGCN (this work) & **94.0** & **68.3** & **89.8(0.85)** & **95.7** & **95.7** & **95.7(0.34)** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result on English datasets. On both benchmarks, the F1-score for either class and the overall accuracy are given in this table. “L” and “M” denote literal and metonymic class, respectively. +NER+POS means integrating both NER and POS features with the baseline model. In general, due to the application of advanced pre-trained language representation along with soft dependency clues, EBAGCN obtains best results. effect due to the incorporation of entity constraint, which greatly improves the accuracy of the model. Furthermore, the three models also perform differently. The experiment on EBGCN produces a decent accuracy that is 0.3% and 0.2% higher than Entity BERT on SemEval and ReLocaR, which illustrates that the application of GCN helps improve performance by catching the ignored information from syntax. Moreover, EBAGCN obtains an improvement of 0.7% and 0.2% compared with EBGCN in terms of accuracy. This fact provides ample proof that the introduction of the multi-head attention mechanism assists GCNs in learning better information aggregations by simultaneously pruning irrelevant information and emphasizing dominating relations concerning indicators such as verbs in a soft method. The table also gives the F1-score for literal and metonymic results, respectively. The consequence shows that EBAGCN achieves the best F1-score on SemEval (metonymic accounts for 20%) and ReLocaR (metonymic accounts for 50%), which suggests that EBAGCN is adaptive in various class distributions.