Toward Building a Legal Knowledge-Base of Chinese Judicial Documents for Large-Scale Analytics

Authors

• Amarnath Gupta, University of California, San DiegoFollow
• Alice Z. Wang, Columbia Law School
• Kai Lin, University of California, San Diego
• Haoshen Hong, University of California, San Diego
• Haoran Sun, University of California, San Diego
• Benjamin L. Liebman, Columbia Law SchoolFollow
• Rachel E. Stern, University of California, BerkeleyFollow
• Subhasis Dasgupta, University of California, San Diego
• Margaret Roberts, University of California, San DiegoFollow

Document Type

Book Chapter

Publication Date

2017

Abstract

We present an approach for constructing a legal knowledge-base that is sufficiently scalable to allow for large-scale corpus-level analyses. We do this by creating a polymorphic knowledge representation that includes hybrid ontologies, semistructured representations of sentences, and unsupervised statistical extraction of topics. We apply our approach to over one million judicial decision documents from Henan, China. Our knowledge-base allows us to make corpus-level queries that enable discovery, retrieval, and legal pattern analysis that shed new light on everyday law in China.

Disciplines

Comparative and Foreign Law | Criminal Law | Law

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Recommended Citation

Amarnath Gupta, Alice Z. Wang, Kai Lin, Haoshen Hong, Haoran Sun, Benjamin L. Liebman, Rachel E. Stern, Subhasis Dasgupta & Margaret Roberts, Toward Building a Legal Knowledge-Base of Chinese Judicial Documents for Large-Scale Analytics, JURIX 2017: The Thirtieth Annual Conference, Adam Wyner & Giovanni Casini (Eds.), IOS Press (2017).
Available at: https://scholarship.law.columbia.edu/faculty_scholarship/4833

DOI

https://doi.org/10.3233/978-1-61499-838-9-135