## 4 Results
### Confusion Matrix Analysis  
The confusion matrix is typically used for classification tasks [20], where the horizontal axis (\(X\)-axis) represents the model's predicted label and the vertical axis (\(Y\)-axis) represents the true label. The values (shades of color) in each cell of the matrix represent the number or frequency of occurrences of the model when \"True Label = \(Y\)-axis for this category\" and \"Predicted Label = \(X\)-axis for this category\". Typically, if the model is more accurate, the confusion matrix will appear darker (more often) on the main diagonal (i.e., where the true labels are the same as the predicted labels) and lighter on the off-diagonal. The code plots the confusion matrix by selecting the top_k most frequent tokens (by default, the top 10), which are counted from the \"true labels of valid tokens\". That is, if a token (e.g., a kanji or subword unit) has the highest frequency of occurrence in the entire test set, it will be included in the top 10. When the matrix is drawn, only these tokens are shown in the rows and columns, and other, less frequent tokens are not shown in the matrix. The horizontal axis is the predicted label and the vertical axis is the real label. The darker the color, the more occurrences of the (real label, predicted label) pair. If the main diagonal is relatively dark, it means that the model's prediction accuracy on these common tokens is highly accurate; if there are certain dark grids outside the diagonal, it means that these tokens are easy to be misclassified as other tokens. As shown in the figure above, the horizontal axis (predicted labels) represents the categories predicted by the model; the vertical axis (true labels) represents the true categories of the sample (e.g., \"\(\sharp\)\", \"\(\sharp\)\", \"\(\sharp\)\", \"\(\sharp\)\", etc.). The cell values represent the number of times a true category was predicted to be a category. For example, the number of times the true category \"\(\sharp\)\" is predicted to be \"\(\sharp\)\" is eight. Right color scale: the darker the color, the larger the true sample size of the category (e.g., \"ground\" has the largest sample size and the darkest color). The darker the color, the larger the true sample size of the category (e.g., \"\(\sharp\)\" has the largest sample size and the darkest color). From this, we can see that during the model training process, the number of times \"\(\sharp\)\" is accurately predicted as the category \"\(\Uparrow\)\" is 22, and the number of times it is incorrectly identified as \"\(\Uparrow\)\" is 1, while all other categories are almost accurately predicted, which means that the former category is not accurately predicted. Almost all other categories are accurately predicted, indicating that the model has high accuracy in the first 10 high-frequency categories. There are two reasons for the incorrect recognition of \"\(\Uparrow\)\" and \"\(\Uparrow\)\". First, from a phenological perspective, \"\(\Uparrow\)\" and \"\(\Uparrow\)\" are both structural particles in Chinese that share the same pronunciation (\"de\") but serve distinct grammatical roles: \"\(\Uparrow\)\" is typically used as an attributive marker (e.g., modifying nouns, as in \"\(\Uparrow\)\" [beautiful flower]), while \"\(\Uparrow\)\" serves as an adverbial marker (e.g., modifying verbs or adjectives, as in \"\(\Uparrow\)\" [happily laugh]). However, in real-world usage, these particles are often confused due to contextual ambiguity. For instance, in informal or colloquial contexts (e.g., social media, spoken language, or non-standard writing), users may interchange them arbitrarily, blurring the grammatical boundaries. Specific scenarios, such as when an adjective is used adversvially without clear morphological cues (e.g., \"\(\Uparrow\)\" [h]\(\Uparrow\)\" [h]\(\Uparrow\)\"), can lead to ambiguity that challenges both human annotation and model training.