## 4 Experiments
### Results
#### 4.3.2 Ablation Experiments  
**(1) Effectiveness experiment for word distribution features**  
For the detection of dictionary-based AGDs, we first performed meaningful word segmentation and calculated the context-sensitive word embedding features. Then, we calculated the standard deviation (Std) of each dimension of word features to measure the correlation between words based on the Koh method. The mean of each dimension (Mean) was calculated to represent the distribution of word features. The word segmentation methods we considered were WordSegment and Wordinja. We assessed the performance of six settings: WordSegment (Mean), WordSegment (Std), WordSegment (Mean+Std), Wordinja (Mean), Wordinja (Std), and Wordinja (Mean+Std). For each setting of the experiments, two fully-connected layers were used for classification after the word distribution features were obtained. The experimental results are shown in Table 7.  
Based on the results, the performance of Wordinja was significantly better than that of WordSegment. In addition, for both Wordinija and WordSegment, the fusion of the Mean and Std features effectively improved the detection effect of the model compared to using only Std or Mean as features. Therefore, Wordinja (Mean+Std) was selected as the final feature in this experiment. In addition, Wordinja (Mean) was similar to the Koh method, and the Wordinja (Mean) method had two more fully-connected layers than the Koh method. In contrast, the performance of Wordinja (Mean) was slightly worse than that of the Koh method. Based on our analysis, we believe that it affects the detection ability after increasing the complexity of the model when only the mean values are used as features. However, after fusing the mean and Std features, the detection performance was significantly better than that of the Koh method, which demonstrated the effectiveness of the fusion of the two kinds of features. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Indicators** & **WordSegment (Mean)** & **WordSegment (Std)** & **WordSegment (Mean+Std)** & **Wordinija (Mean)** & **Wordinija (Mean+Std)** \\ \hline \multirow{4}{*}{Dictionary -based dataset} & Accuracy & 93.09\% & 93.80\% & 94.73\% & 93.52\% & 94.90\% & 95.00\% \\ \cline{2-8}  & Precision & 93. Based on the results, the 1-gram method performed better on the character-based dataset, while the 3-gram method performed better on the dictionary-based dataset. We believe that when the value of \(n\) in the n-gram increases, the detection performance of dictionary-based AGDs increases, while the performance of character-based AGDs decreases. Therefore, we achieved good detection performance for the two kinds of AGDs at the same time by fusing multiple kinds of n-grams. Comparing the results of 1+3-gram and 1+2+3-gram, we found that 1+3-gram effectively detected both kinds of AGDs. After adding 2 grams, the accuracy and F1 value were only slightly improved, which did not significantly improve the overall performance of the model but greatly increased the complexity of the model. Therefore, in the final model, we gave priority to fusing 3-grams and 1-grams and finally considered 2-grams. **(3) Effectiveness experiment for feature fusion**  
In this experiment, we compared the detection performance of different feature fusion methods. The features we chose included word distribution features, statistical features, and n-gram features. Based on the results in experiments (1) and (2), we first used Wordninja (Std+Mean), which was the best word distribution feature, as the basic method, and then we sequentially added statistical features, 3-gram sequence features, 1-gram sequence features, and 2-gram sequence features. We compared the performance before and after each feature was added, and then the optimal feature fusion method was selected. The experimental results are shown in Table 9.