Abstract
Recent neural Open Information Extraction (OpenIE) models have improved traditional rule-based systems significantly for Chinese OpenIE tasks. However, these neural models are mainly word-based, suffering from word segmentation errors in Chinese. They utilize dependency information in a shallow way, making multi-hop dependencies hard to capture. This paper proposes a Multi-Grained Dependency Graph Neural Network (MGD-GNN) model to address these problems. MGD-GNN constructs a multi-grained dependency (MGD) graph with dependency edges between words and soft-segment edges between words and characters. Our model makes predictions based on character features while still has word boundary knowledge through word-character soft-segment edges. MGD-GNN updates node representations using a deep graph neural network to fully exploit the topology structure of the MGD graph and capture multi-hop dependencies. Experiments on a large-scale Chinese OpenIE dataset SpanSAOKE shows that our model could alleviate the propagation of word segmentation errors and use dependency information more effectively, giving significant improvements over previous neural OpenIE models.
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Notes
- 1.
We express n-ray extraction as tuple and binary extraction as triple in this paper.
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Acknowledgements
This work is supported by the NSFC Projects (U1736204, 62006136), grants from Beijing Academy of Artificial Intelligence (BAAI2019ZD0502) and the Institute for Guo Qiang, Tsinghua University (2019GQB0003).
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Lyu, Z., Shi, K., Li, X., Hou, L., Li, J., Song, B. (2021). Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_13
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