Rules still work for Open Information Extraction

2024-03-16

arXiv.org

AI Summary

To address the bottlenecks in Chinese Open Information Extraction (OIE)—namely, syntactic complexity, overreliance on manually crafted patterns, or opaque end-to-end black-box modeling—this paper proposes APRCOIE, a novel OIE framework. First, it introduces a linguistically grounded extraction pattern formalism tailored to Chinese syntactic characteristics. Second, it designs an efficient tensor-based coarse-graining mechanism to drastically reduce redundant matching overhead. Third, it pioneers a fully automated pattern construction paradigm, eliminating dependence on predefined rules or supervised annotations. To enable robust training and evaluation, we construct the first large-scale, human-annotated Chinese OIE benchmark dataset. Experiments demonstrate that APRCOIE consistently outperforms state-of-the-art methods across Precision, Recall, and F1. Both the code and dataset are publicly released, advancing Chinese OIE toward greater interpretability, computational efficiency, and annotation independence.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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)

Problem

• Chinese Open Information Extraction
• Accuracy Improvement
• Complex Grammar Handling

Innovation

• APRCOIE
• Chinese Open Information Extraction
• Optimized Grammar Processing