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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1466))

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Abstract

Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP. Extensive experimental results on the dataset show that our approach achieves better performance compared with baselines. The code and dataset are available in https://github.com/zjunlp/LegalPP for reproducibility.

L. Li, Z. Bi and H. Ye—Equal contribution and shared co-first authorship.

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Notes

  1. 1.

    https://www.gdzwfw.gov.cn/.

  2. 2.

    https://www.dgl.ai/.

  3. 3.

    https://github.com/google-research/bert.

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Correspondence to Shumin Deng .

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Li, L., Bi, Z., Ye, H., Deng, S., Chen, H., Tou, H. (2021). Text-Guided Legal Knowledge Graph Reasoning. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. CCKS 2021. Communications in Computer and Information Science, vol 1466. Springer, Singapore. https://doi.org/10.1007/978-981-16-6471-7_3

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  • DOI: https://doi.org/10.1007/978-981-16-6471-7_3

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