## 4 Results and Discussion
### Automatic Recognition of Lexical Situation Types
#### 4.3.2 Classification Experiments Based on Embedding Vectors  
Considering the complexity of predicting situation types, we have decided to explore semantic representation methods based on embedding vectors, aiming to fully explore the latent deep features within the data to enhance the model's accuracy in predicting different situation types. Among the four models, Word2 vec and FastText are early word vector models that have played a significant role in research within the field of natural language processing. With the rapid advancement of deep learning technologies, more advanced models have emerged and gradually become mainstream. Therefore, we have introduced BERT and ChatGPT models for enhanced comparative analysis. The experimental framework maintained methodological continuity with the procedure in Section 3.3.1, wherein pandas' DataFrame structures standardized feature processing, and scikit-learn's GridSearchCV class optimized classifier parameters. To avoid overfitting in the experiments, we used tenfold cross-validation to ensure the evaluation rigor. Moreover, random seeds were fixed across three classifiers for reproducibility. Tables 7 and 8, respectively, present the specific parameter settings of the classifiers used with different datasets and the experimental results. A comparison between Tables 6 and 8 reveals that the experimental accuracy of embedding vectors significantly surpasses that of linguistic feature vectors. Table 8 demonstrates that the tenfold cross-validation results using FastText word vectors are relatively superior, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vector** & **Classifier** & **MNLogit** & **SVM** & **ANN** \\ \hline \multirow{4}{*}{Word2 vec vectors} & C = 1, penalty = 12, & C = 5, gamma = scale, & \begin{tabular}{c} activation = logistic, \\ alpha = 0.0001, \\ hidden layer sizes = (300), \\ learning\_rate = constant, \\ solver = sgd \\ \end{tabular} \\  & & & & \\ \multirow{4}{*}{FastText vectors} & C = 1, penalty = 11, & C = 5, gamma = scale, & \begin{tabular}{c} activation = relu, alpha = 0.01, \\ hidden layer sizes = (200), \\ learning\_rate = adaptive, \\ solver = sgd \\ \end{tabular} \\  & & & & \\ \multirow{4}{*}{Bert vectors} & C = 0.1, penalty = 12, & C = 10, gamma = auto, & \
\begin{tabular}{c} activation = logistic, \\ alpha = 0.0001, \\ hidden layer sizes = (300), \\ learning\_rate = adaptive, \\ solver = sgd \\ \end{tabular} \\  & & & & \\ \multirow{4}{*}{ChatGPT vectors} & C = 10, penalty = 11, & C = 10, gamma = scale, &
\begin{tabular}{c} activation = logistic, alpha = 0.01, \\ hidden layer sizes = (100), \\ learning\_rate = constant, \\ solver = adam \\ \end{tabular} \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameter settings for different classifiers based on embedding vectors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Classifier** & **MNLogit** & **SVM** & **ANN** \\ \hline \multirow{4}{*}{Word2 vec vectors} & Word2 vec vectors & 82.08\% & 82.61\% & 93.03\% \\  & FastText vectors & 80.14\% & 82.88\% & 99.56\% \\ \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Bert vectors & 81.64\% & 81.11\% & 97.53\% \\  & ChatGPT vectors & 81.73\% & 80.76\% & 99.38\% \\ \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Baseline & 72.64\% & 72.64\% & 72.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification experiment results based on embedding vectors. exhibiting higher experimental accuracy on both SVM and ANN classifiers compared to the other three word vector matrices. In particular, the ANN classifier achieves 99.56% accuracy in prediction, marking an increase of 26.92% in accuracy compared to the baseline. This remarkable performance may be attributed to FastText's advantage in handling word morphology information. In response to the occurrence of high accuracy in prediction experiments using FastText word vectors, we need to test whether the model is experiencing overfitting.