# Rules still work for Open Information Extraction

arXiv: https://arxiv.org/abs/2403.10758 · HTML: https://mlsanity.com/paper/2403.10758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [Span Model for Open Information Extraction on Accurate Corpus](https://mlsanity.com/paper/1901.10879/) (2019)
- [Answering Complex Questions Using Open Information Extraction](https://mlsanity.com/paper/1704.05572/) (2017)
- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [WiRe57 : A Fine-Grained Benchmark for Open Information Extraction](https://mlsanity.com/paper/1809.08962/) (2018)
- [BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation](https://mlsanity.com/paper/2109.06850/) (2021)
- [OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference](https://mlsanity.com/paper/1904.12606/) (2019)
- [Graphene: Semantically-Linked Propositions in Open Information Extraction](https://mlsanity.com/paper/1807.11276/) (2018)

## Abstract (arXiv)

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)