# An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion
## 4 Experiments
### Overall Results  
In this paper, a series of experiments are carried out on the CoNLL04 dataset and the ADE dataset. The data is randomly divided. Sixty percnet of them is used as training part, so the development and test part share the remains. To verify the effect of proposed method based on dynamic context, this paper explores performance of some similar model. As shown in Table 2, this part makes a comparision with some competing results to evaluate whether the dynamic context has an enhancement to relation extraction model. The first column refers to the applied datasets. In the second column, some ERE methods are exhibited, i.e., previous study and proposed method. The results of the relation extraction \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train** & **Dev** & **Test** \\ \hline CoNLL04 & 922 & 231 & 288 \\ ADE & 2563 & 804 & 905 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The detail in some datasets. A series of experiments are carried out on them. are presented in last three column, i.e., precision, recall and F1. In addition, the best results for all datasets are marked with bold font. Comparing with the methods cited and baseline comprehensively, the SPERT-DC has a best performance on two datasets, the results of which are presented in Table 2. On the CoNLL04 dataset, the SPERT-DC outperforms the model [13] which also relies on BERT as word embedding by up to 3.6%. In addition, SPERT-DC improves on both precision and recall. This paper has a better performance compared with the model [26] which uses adversarial training to enhance robustness for ERE tasks on two datasets. On the ADE dataset, the method in this paper also has a greater improvement in F1-scores than the model [12] based on Bi-LSTM-CRF, indicating that the BERT surpasses traditional network in feature extraction, such as LSTM, RNN et al. Meanwhile, SPERT-DC outperforms the baseline model by about 1%. This paper also replaces the CLS representation by full-context, i.e., the a max pooling over the sentence's all tokens to conduct a host of studies, the results of which are shown in Figure 5. It is found that the cls-mid combination is higher than the full-mid combination on F1-scores, indicating that the CLS representation is more suitable for text context. Some thresholds in 0-1 range are taken to test, result of which is shown in Figure 6 and reveals a performance in form of a broken line. A good result is seen while the threshold T \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **P** & **R** & **F1** \\ \hline \multirow{4}{*}{CoNLL04} & paper [12] & 72.99 & 63.37 & 67.01 \\  & paper [13] & 69.20 & 68.20 & 68.90 \\ \cline{1-1}  & paper [26] & - & - & 67.99 \\ \cline{1-1}  & Baseline & 73.04 & 70.00 & 71.47 \\ \cline{1-1}  & SPERT-DC & 75.05 & 70.21 & **72.55** \\ \hline \multirow{4}{*}{ADE} & paper [7] & 67.50 & 75.80 & 71.40 \\ \cline{1-1}  & paper [12] & 72.10 & _77.24_ & 74.58 \\ \cline{1-1}  & paper [26] & - & - & 75.52 \\ \cline{1-1}  & Baseline & 78.09 & 80.43 & 79.24 \\ \cline{1-1}  & SPERT-DC & 76.82 & 84.08 & **80.28** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The detail in result of experiments. The best results for all datasets are marked with bold font. Figure 5: The tendency of F1-scores of two context combination pattern. The cls-mid (blue) performs better than the full-mid (orange). is in 0-0.1 range and the F1-score peaks in case of zero. Therefore, this paper takes 0 as the final threshold for follow-up study. As shown in Figure 7, in order to evaluate the performance of model, this paper conducts some experiments on many sentences varying in length. Before the Bi-LSTM_ATT is added, the experiment on longer texts has a poor result. The downward trend slows down after it is add, which leads to significant improvements. As shown in Figure 8, the ROC curve for each model of the two datasets is determined in order to further proves the superiority of proposed method on entity relation extraction.