## 4 Results and Discussion
### Automatic Recognition of Lexical Situation Types
#### 4.3.3 Automatic Recognition of Situation Types of Unknown Language Data  
We extracted 2000 high-frequency verbs from the CCL corpus as the objects of prediction experiments. We obtained corresponding word vectors from the pre-trained FastText model to form the prediction set, which was then divided into several parts for machine-based automatic recognition in batches. After each part of the predictions, the automatic recognition results of situation types were verified by four annotators with subsequent consistency checks. Once confirmed to be accurate, they were merged into the training set. By gradually expanding the size of the training set in this manner, we continuously improve the model's predictive performance. After the completion of predictions, we obtained 2000 situation type labels corresponding to the verbs. We merged this portion of data with the original training set, resulting in the complete dataset for this prediction experiment. The dataset comprises 3133 verbs, their corresponding FastText word vectors and situation type labels, including 1839 activity verbs, 349 state verbs and 945 transition verbs. To verify whether the model can still achieve high accuracy in automatic recognition of situation types on the expanded dataset, we conducted tenfold cross-validation using the merged dataset as the basis and an ANN classifier again. Table 9 and Figure 4 present the detailed results of the experiment. Table 9 demonstrates that the model performs exceptionally well on multiple evaluation metrics. Specifically, the Precision is 98.61%, and the Recall reaches 96.84%, indicating the model's high reliability and effectiveness in automatic recognition of situation types. As a comprehensive measure of Precision and Recall, the F1 score evaluates the model's balanced performance. The F1 score in the table is 97.68%, suggesting that the model's overall performance in handling situation type prediction tasks is quite satisfactory. With an Accuracy of 98.37%, it shows that in the testing of 3133 verb samples, the model's proportion of correct predictions is significantly high, with less than 2% of samples having their situation types incorrectly predicted. Compared to the baseline, the accuracy in this experiment has increased by nearly 40%, demonstrating the model's strong predictive ability in automatic recognition of verb situation types and validating the scientific validity of the selected dataset and algorithm combination. The confusion matrix in Figure 4 illustrates the specific classification performance, revealing that the model's predictive performance on activity verbs is quite outstanding, with only 1 verb sample incorrectly predicted as a state verb and 17 verbs incorrectly predicted as transition verbs. The model's accuracy in recognizing state verbs is lower than that of activity situations, with 10 samples incorrectly predicted as activity verbs and 15 samples incorrectly predicted as transition verbs. The model demonstrates relatively good performance on transition verbs, with 19 samples incorrectly predicted as activity verbs. However, the accuracy is still lower compared to the model's performance on activity verbs. Overall, the model exhibits strong recognition capabilities in activity situation  
Figure 4: Confusion matrix analysis for verb classification on 3133 linguistic samples. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Parameters** & **Precision** & **Recall** & **F1** & **Accuracy** & **Baseline** & **SOA** \\ \hline \multirow{4}{*}{ANN} & activation = relu, alpha = 0.001, & & & & & & \\  & hidden layer sizes = (300), & & & & & & \\  & learning\_rate = adaptive, & 98.61\% & 96.84\% & 97.68\% & 98.37\% & 58.70\% & 72.05\% \\  & solver = sgd & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameter configuration and experimental results of verb prediction task on 3133 samples. and transition situation, while the prediction performance on state situation lags slightly behind.