Rules still work for Open Information Extraction
Jialin Hua Liangqing Luo Weiying Ping Yan Liao Chunhai Tao Xuewen Lub
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 (https://github.com/jialin666/APRCOIE_v1)