## 3 Procedure, Data and Methodology
### Construction of the Training Dataset  
After establishing the verb situation type system using statistical methods, the first research question of this paper has been addressed. The next step is to tackle the problem of achieving high-accuracy prediction of verb situation types. The second research question can be broken down into two specific issues: first, how to construct a gold-standard dataset for model training. To improve the prediction accuracy of situation types, it is essential to have a large-scale, high-quality validation dataset as a foundation. We will attempt to extract verb vectors using both supervised and unsupervised methods to build the situation type dataset and compare the effectiveness of different vectors in prediction experiments. Second, what methods can be employed during model training to achieve better performance and improve the accuracy of automatic verb situation type identification? We will integrate statistical methods and computationalalgorithms to explore approaches for improving model performance. Specifically, we will experiment with different classifiers and conduct tenfold cross-validation to select the best-performing model, which will then serve as the model for large-scale automatic identification of verb situation types. The detailed processes and methodologies related to these two issues will be elaborated on in this section and the following section. This section primarily introduces the data and methods used to construct the gold-standard dataset. Each dataset mainly consists of the following components: verb entries, verb situation type labels, and linguistic feature vectors or embedding vectors of the verbs. Each of these components will be described in detail below. After the automatic generation of the situation type system, the pairing patterns between verb senses and situation types have been established, and the verb entries along with their corresponding situation type labels are naturally preserved. It is worth noting that not all verb sense entries used in the clustering process are retained in the training dataset. This is because some senses occur with extremely low frequency in the corpus, which could lead to a data sparsity problem during classification experiments. Therefore, only the most frequent sense of each verb and its corresponding situation type label are retained in the dataset. We primarily rely on the research by [PERSON] [44] as a reference for selecting high-frequency verb senses. [PERSON] [44] examined 1220 verbs from Levels A and B of The outline of the Graded Vocabulary for HSK, using the State Language Affairs Commission's Modern Chinese General Corpus as the basis. She comprehensively and meticulously analyzed the distribution of verb senses and established a three-level system comprising verbs, their frequencies and the proportion of each sense in polysemous verbs. Based on this system, we selected the primary sense for each verb according to the proportion of senses in polysemous verbs within the corpus. Additionally, for a small number of verbs not covered in [PERSON]'s study [44], we referred to the Modern Chinese Dictionary (7 th Edition) and the dictionary by [PERSON] [40] for sense explanations, combined with frequency statistics from the CCL corpus, and identified the most frequent sense as the primary sense. As for the verb vector data, we employed both supervised and unsupervised methods to construct features for situation type classification. The former refers to extracting linguistic feature vectors from large corpus, while the latter involves extracting embedding vectors of verbs, including Word2 vec, FastText, BERT and ChatGPT. Linguistic feature vectors can not only validate the effectiveness of the linguistic model we propose but also reveal the co-occurrence relationships between situation types and linguistic features, offering strong explanatory power.