## 5 Conclusions  
This paper presents a study focused on achieving high-accuracy prediction of verb situation types, systematically detailing the entire workflow from constructing a verb lexicon,annotating co-occurrence features and automatically generating a situation type system, to implementing automatic situation type identification. We employed rigorous methodologies including consistency checks, hierarchical clustering and tenfold cross-validation to ensure scientific validity throughout the experimental process, which ultimately solved the two research questions raised in this paper. Firstly, this study demonstrates through experiments that semantic distance can serve as an objective criterion for classifying verb situation types. The verb situation type system can be automatically generated through agglomerative hierarchical clustering, with the clustering results indicating that verb situation types can be categorized into activity, state and transition. These clustering results validate previous empirically annotated classification systems. We argue that the tripartite division of verb situation types is a more reasonable classification method. Theoretically, the accomplishment situation type in [PERSON]'s fourfold classification [4] contains phrase structure, which overlooks the compositionality of situation types. This has led many scholars to be unable to explain the issue of the inconsistencies between the situation types of verbs in verb-object structures and those of individual verbs, thus advocating that situation types only exist at the sentence level. To insist on the compositionality of situation types, the situation type classification must originate at the lexical level of verbs rather than operate on phrasal or sentential units. The tripartite classification proposed in this study effectively addresses this issue. From a practical perspective, the experimental results of this study demonstrate that adhering to the tripartite classification of verb situation types can lead to high accuracy in predictive experiments, which holds practical value for both Chinese language research and teaching. Secondly, we demonstrate that verb situation types can be predicted, and high-quality predictions can be achieved by constructing gold-standard datasets and utilizing computational algorithms. Furthermore, we validate that embedding vectors can achieve high accuracy in handling complex semantic relationships and classification tasks. We construct vector matrices using both supervised and unsupervised methods. By comparing the tenfold cross-validation results of different datasets on three classifiers, we evaluate and select the optimized model suitable for automatic recognition of situation types. During the experimental process, we observe that the prediction accuracy of embedding vectors far surpasses that of linguistic feature vectors. After comparison, we ultimately choose the FastText vector dataset with the ANN classifier to complete the prediction task for 2000 new samples. Finally, the model achieves a 98.37% accuracy in the prediction task of 3133 verb samples, surpassing the baseline by nearly 40%. This demonstrates the scientific and operational validity of our dataset construction method and algorithm selection, indicating that the model has attained practical performance. The contributions of this paper mainly include the following aspects:  
Firstly, we validate the realization of aspect at the lexical level in Chinese, directly demonstrating that situation types can be assigned to verbs, contrary to the previous understanding that they can only be assigned to verb phrases or sentences [5; 38]. Additionally, this study indicates that only through detailed annotation of syntactic structures at various levels can a comprehensive and thorough study of aspect be conducted. The research in this paper provides strong support for the compositional view of aspect and also offers an operational paradigm framework for cross-linguistic aspect research.