## 2 Literature Review
### Research Based on Computational Methods  
In recent years, numerous scholars have conducted in-depth studies on situation types by integrating statistical methods and computer algorithms. These studies primarily focus on the following two aspects. Firstly, there are studies of the prediction of situation types. In the field of computational linguistics, the prediction of situation types can be divided into sentence-level and verb-level studies. At the sentence level, relevant research includes [PERSON] and [PERSON] [24], [PERSON] et al. [25] and [PERSON] [26]. These studies emphasized the use of linguistic features and had achieved high prediction accuracy. For example, [PERSON] [26] attained a prediction accuracy of 77.95% in experiments involving the classification of mid-level event types, including static, dynamic, achievement and accomplishment features. However, these scholars mapped the situation types of sentences to the composition of verbs, thereby denying the compositionality and recursiveness of situation types. Additionally, the study of [PERSON] et al. [27] is the first attempt of collaborative bootstrapping in natural language processing application, which involves two heterogeneous classifiers. And the model has yielded good results in analyzing temporal relations in a Chinese multiple-clause sentence. [PERSON] and [PERSON] [28] employed both supervised and unsupervised methods to train two models for the automatic identification of situation types in Italian sentences. At the verb level, research on the prediction of situation types includes studies by [PERSON] et al. [29], [PERSON] and [PERSON] [30], [PERSON] and [PERSON] [31], [PERSON] and [PERSON] [32], [PERSON] et al. [33], [PERSON] and [PERSON] [34] and [PERSON] [6; 7], among others. However, the prediction accuracy in these studies is relatively modest. For instance, [PERSON] [7] trained Word2 vec embeddings on a combined corpus of Sinica and Chinese Gigaword to predict the situation types of verbs, yet the best-performing classifier achieved only a 72.05% prediction accuracy. The low accuracy in [PERSON]'s study stems from multiple factors. For instance, while the Chinese Gigaword corpus contains 1.12 billion words, its scale is not comparable to that of the Centre for Chinese Linguistics PKU (abbreviated as CCL) corpus. This disparity results in insufficient contextual co-occurrence information, which impedes models from capturing deep semantic relationships between words, thereby compromising the effectiveness of Word2 vec embeddings trained on this corpus. Additionally, given the continuous emergence of new large language models, Word2 vec embeddings are becoming less competitive. Situation type prediction experiments should now explore the adoption of more advanced embedding vectors to enhance semantic representation capabilities. In recent years, some scholars have combined situation type theory with statistical analysis methods to further investigate aspectual markers in Chinese. [PERSON] and [PERSON] [35] employed multiple correspondence analysis to examine the temporal characteristics of verbs and the aspectual marker \"ZHE\" in the \"V ZHE V ZHE\" structure in Chinese. They concluded that the aspectual properties of the imperfections marker \"ZHE\" in this particular structure are predicted to be dual-dynamic-and-static, durative and atelic. This study provides quantitative support for earlier introspective research on the classification of aspectual markers and situation types. Secondly, there have been studies expanding situation type theory into practical applications, with relevant contributions from [PERSON] and [PERSON] [30], [PERSON] and [PERSON] [32], [PERSON] et al. [33], [PERSON] et al. [36], among others. For instance, [PERSON] and [PERSON] [30] discussed methods for automatically generating summaries of literary short stories. They reviewed viewpoints from scholars like [PERSON] and [PERSON] [37], arguing that verbs are categorized into different situation classes based on dynamic, durative and telic features.