## 4 Results and Discussion
### Automatic Recognition of Lexical Situation Types
#### 4.3.3 Automatic Recognition of Situation Types of Unknown Language Data
Nevertheless, the overall predictive results still indicate the model's high accuracy, providing reliable support for the automatic recognition of situation types. At this point, a practical model dedicated to achieving high accuracy prediction of Chinese verb situation types has been successfully trained. In previous studies, many scholars have proposed automatic recognition models for situation types in different languages. Compared to these prediction systems, our model has been optimized in several key aspects:  
Firstly, at the prediction level, previous research primarily focused on analyzing entire sentences, allowing models to leverage the rich contextual features within sentences to achieve high accuracy easily. However, this approach is not effective in predicting the situation types of basic units. In contrast, predicting verb situation types poses a greater challenge. This study starts from verbs and successfully predicts situation types, thereby offering possibilities for extending predictions to higher-level situation types in the future. Furthermore, the prediction accuracy of models in previous studies was not high and did not surpass the accuracy achieved in our experiments focusing on verb-level prediction. Secondly, concerning the construction of the dataset, previous researchers did not take sufficient measures to reduce subjectivity during the annotation stage. For instance, [PERSON] and [PERSON]'s [28] annotation work was completed by a single annotator, while [PERSON] [26], despite conducting Kappa consistency tests after annotation, only involved two annotators. Additionally, issues such as a lack of transparency in the dataset construction process and small dataset size were present. In contrast, the data annotation process in this study was more standardized in terms of workflow and methods, involving four professional annotators and conducting multidimensional consistency checks to ensure accuracy. Furthermore, we have provided detailed transparency on the construction process of the training dataset in our prediction experiments, facilitating reproducible research by other scholars. Thirdly, current research on predicting situation types is predominantly conducted within the realm of English, with a few studies involving languages such as Italian [28]. Chinese, being the most widely spoken language in the world, has received relatively limited attention in related studies. Presently, the only other researcher focusing on predicting Chinese situation types is [PERSON] [26]; however, their study solely delves into sentence-level scenarios. Our paper enriches the landscape of research on predicting Chinese verb situation types. Fourthly, the research methods adopted by previous scholars are relatively outdated. Firstly, in aspectual classification tasks, the classifiers used by previous researchers are not advanced enough; for instance, [PERSON] [25] and [PERSON] [26] mainly employed SVM classifiers. ANN classifiers often exhibit superior performance in classification tasks and played a crucial role in the prediction experiments of this paper, yet they are rarely mentioned in existing prediction studies. Secondly, the approach taken by previous scholars in constructing vector matrices is rather simplistic, mostly relying on linguistic features and co-occurrence word frequency matrices as training datasets, with limited incorporation of high-dimensional embedding vectors, thus failing to effectively validate the ability of large language models to capture semantic information. In conclusion, the Chinese verb situation type prediction model trained in this paper demonstrates a certain level of advancement and superiority.