## 3 Procedure, Data and Methodology
### Construction of the Training Dataset
#### 3.2.2 Extraction of Embedding Vectors  
While the vector matrix constructed through supervised methods effectively reflects the collocation patterns of verbs with aspectual markers in actual contexts, this approach still has certain limitations when applied to large-scale situation type prediction. This is because the linguistic features of verbs we selected are based on specific contextual information, which overlooks broader contextual cues; thus, the information contained is still not comprehensive enough. Therefore, we will next leverage semantic vectors to conduct further experiments on situation type prediction. Semantic vector representation is a general term for techniques that map linguistic units to real-valued vectors for modeling semantic features. Traditional methods, such as Word2 Vec and FastText, generate static word vectors through unsupervised learning based on the Distributional Hypothesis [45]. This hypothesis posits that a word's semantics are determined by its context, such that words occurring in similar contexts should obtain proximate vector representations. In contrast, pre-trained language models, such as BERT and ChatGPT, leverage deep neural networks to capture dynamic contextual features. Although the two types of models differ in their technical approaches, they both essentially achieve semantic representation through vector space mapping. For ease of comparative analysis, we refer to the vectors generated by these models as \"embedding vectors\" and construct multi-source datasets to comprehensively evaluate the impact of different semantic representation methods on verb situation type prediction. (1) Word2 vec Vectors  
Word2 vec is a classic word embedding model. We utilized Word2 vec to construct word embedding vectors in an unsupervised manner, which not only captures semantic correlations based on word co-occurrence information but also effectively preserves the influence of word frequency. Since the CCL corpus is significantly larger than the Sinica corpus, and larger corpora generally yield higher-quality word vectors, this helps the model better learn subtle semantic distinctions. Therefore, we pre-trained the Word2 vec model on the CCL corpus to obtain the word vectors. (2) FastText Vectors  
Although Word2 vec has achieved relatively good results in previous prediction experiments, it still has certain limitations. The Word2 vec model generates a vector for each individual word but overlooks the internal morphological features of words. The FastText model effectively addresses this limitation. It is suitable for training on large-scale text, adapts better to multilingual text, and performs quickly and accurately on categorization tasks. We trained the FastText model on the CCL corpus to obtain the corresponding word vectors. (3) BERT Vectors  
The BERT model, released by Google in 2018, is a pre-trained language model based on the Transformer architecture, utilizing a bidirectional attention mechanism. Compared to the Word2 vec and FastText models, the BERT model typically possesses stronger context awareness. We utilized the Chinese pre-trained BERT-wwm model, based on the Whole Word Masking technique released by the Joint Laboratory of Harbin Institute of Technology and iFLYTEK Research, to extract verb vectors. (4) ChatGPT Vectors  
Word2 vec and FastText are two traditional static word vector models that do not change once training is completed. In contrast, the word vectors of ChatGPT are dynamic and based on the Transformer model. Compared to the previous three word vector models, ChatGPT utilizes a larger training corpus, making it suitable for tasks such as predicting verb situation types. We extracted the corresponding word vectors from ChatGPT to construct the dataset for verb situation type automatic recognition experiments. With this, all four datasets for the automatic recognition of verb situation types have been fully constructed.