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.

本文提出了显式和隐式命题的概念，基于语言学和逻辑提出了一种综合的多层次命题分类系统，同时创建了一个包含所有与命题相关类别的大规模中文命题数据集 PEACE 进行评价。研究结果表明适当建模命题的语义特征非常重要，BERT 在命题分类能力上相对较好，但缺乏跨领域的可迁移性，而 ChatGPT 在分类能力上表现较差，但可以通过提供更多的命题信息来改善。许多问题仍然有待进一步研究。

从汉语角度的命题：一个汉语命题分类评估基准

Proposition from the Perspective of Chinese Language: A Chinese  Proposition Classification Evaluation Benchmark

We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.

Graphene 是一种基于 Open IE 系统的轻量级的语义表述，能够生成准确、有意义和完整的命题，并维护其上下文和各种语言关系，从而提高所提取命题的表现力。

基于图的上下文保持开放式信息抽取系统

Graphene: A Context-Preserving Open Information Extraction System

We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.

本文介绍了一种使用两层转换阶段的开放式信息提取方法，其中包括从复杂的语言结构中提取命题的核心关系元组和上下文信息，以及使用修辞关系将其语义相关。我们的参考实现 Graphene 在正确构建 n - 元谓词 - 参数结构方面优于现有的开放式 IE 系统，并且我们的框架的转换过程对现有的开放式 IE 方法会产生益处。