  Existing propositions often rely on logical constants for classification.
Compared with Western languages that lean towards hypotaxis such as English,
Chinese often relies on semantic or logical understanding rather than logical
connectives in daily expressions, exhibiting the characteristics of parataxis.
However, existing research has rarely paid attention to this issue. And
accurately classifying these propositions is crucial for natural language
understanding and reasoning. In this paper, we put forward the concepts of
explicit and implicit propositions and propose a comprehensive multi-level
proposition classification system based on linguistics and logic.
Correspondingly, we create a large-scale Chinese proposition dataset PEACE from
multiple domains, covering all categories related to propositions. To evaluate
the Chinese proposition classification ability of existing models and explore
their limitations, We conduct evaluations on PEACE using several different
methods including the Rule-based method, SVM, BERT, RoBERTA, and ChatGPT.
Results show the importance of properly modeling the semantic features of
propositions. BERT has relatively good proposition classification capability,
but lacks cross-domain transferability. ChatGPT performs poorly, but its
classification ability can be improved by providing more proposition
information. Many issues are still far from being resolved and require further
study.
