Open information extraction (OIE) aims to extract surface relations and their
corresponding arguments from natural language text, irrespective of domain.
This paper presents an innovative OIE model, APRCOIE, tailored for Chinese
text. Diverging from previous models, our model generates extraction patterns
autonomously. The model defines a new pattern form for Chinese OIE and proposes
an automated pattern generation methodology. In that way, the model can handle
a wide array of complex and diverse Chinese grammatical phenomena. We design a
preliminary filter based on tensor computing to conduct the extraction
procedure efficiently. To train the model, we manually annotated a large-scale
Chinese OIE dataset. In the comparative evaluation, we demonstrate that APRCOIE
outperforms state-of-the-art Chinese OIE models and significantly expands the
boundaries of achievable OIE performance. The code of APRCOIE and the annotated
dataset are released on GitHub (this https URL)

本研究提出了一种创新的 APRCOIE 模型，专门用于处理中文文本，通过自动生成抽取模式，可以处理各种复杂多样的中文语法现象，与现有的中文 OIE 模型相比，APRCOIE 表现更好并显著扩展了 OIE 性能的界限。