## 4 Experiments
### Results  
#### 4.3.1 Comparison Experiments with Existing Detection Methods  
**(1) Comparison experiment for detection effects**  
The comparison methods we implemented were the Koh method [20] based on meaningful word segmentation, the ATT-CNN-BiLSTM method [34] based on local features extracted by CNN, and the LSTM.MI method [5] based on character LSTM. Our method was implemented based on the PyTorch and Allennlp packages. The initial learning rate was set to 0.001, and [PERSON] was selected as the optimizer. During the training stage, a total of 25 epochs were iterated, and the learning rate decayed to 0.1 of the previous rate every 5 iterations. The source code for the LSTM.MI method was released based on Keras, and we directly used the original implementation for experiments. For the Koh method, we reimplemented the method in the paper. For the implementation of ELMo, we chose two methods: one was based on TensorFlow_hub [56] as described in the paper, and the other was based on Allennlp with PyTorch, which was the same as our method. We found that the effect of ELMo implemented based on PyTorch was better in experiments, so we chose it as the final implementation. For the ATT-CNN-BiLSTM method, we referred to the source code of LSTM.MI and reimplemented the method based on Keras according to the description in the paper. The performances of the four methods are shown in Table 3.  
In all tables of the experimental results, the best result is shown in red, and the second-best result is shown in blue. Based on the results of the comparison, we found that our method achieved the highest detection performance on all three datasets. The Koh method uses the mean values of the context-sensitive word embedding features of the words as the final features, and it achieved better detection performance for dictionary-based AGDs, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Indicators** & **Ours** & **Koh** & \begin{tabular}{c} **ATT-CNN-** \\ **BiLSTM** \\ \end{tabular} & **LSTM.MI** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Dictionary-based \\ dataset \\ \end{tabular} } & Accuracy & 96.33\% & 94.39\% & 94.13\% & 93.09\% \\ \cline{2-6}  & Precision & 96.34\% & 94.34\% & 93.61\% & 92.88\% \\ \cline{2-6}  & Recall & 96.64\% & 94.95\% & 95.25\% & 93.96\% \\ \cline{2-6}  & F1 & 96.49\% & 94.64\% & 94.43\% & 93.42\% \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Character-based \\ dataset \\ \end{tabular} } & Accuracy & 98.64\% & 96.79\% & 98.13\% & 98.42\% \\ \cline{2-6}  & Precision & 98.68\% & 96.72\% & 98.53\% & 98.57\% \\ \cline{2-6}  & Recall & 98.56\% & 96.78\% & 97.68\% & 98.23\% \\ \cline{2-6}  & F1 & 98.62\% & 96.75\% & 98.10\% & 98.40\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Full dataset \\ \end{tabular} } & Accuracy & 97.24\% & 94.63\% & 96.14\% & 96.12\% \\ \cline{2-6}  & Precision & 97.22\% & 94.78\% & 96.16\% & 96.72\% \\ \cline{2-6}  & Recall & 97.25\% & 94.46\% & 96.10\% & 95.47\% \\ \cline{2-6}  & F1 & 97.23\% & 94.62\% & 96.13\% & 96.09\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of comparison experiments. while the detection effect of character-based AGDs was relatively poor. The LSTM.MI method uses the traditional character sequence detection method, so it exhibited relatively good detection performance for character-based AGDs. The ATT-CNN-BiLSTM method achieved better results on the full dataset by fusing local features and sequence features. By the effective fusion of the multiple features, our method significantly improved the performance of both the dictionary-based dataset and the full dataset. Since only 1-gram sequence features in our method were specifically for the detection of character-based AGDs and the gain of other features on character-based AGDs was relatively small, the performance of our method was only slightly better than that of the LSTM.MI method.