An entity-centric approach to manage court judgments based on Natural Language Processing

In this paper, we present an entity-centric infrastructure to manage legal documents, especially court judgments, based on the organization of a textual document repository and on the annotation of these documents to serve a variety of downstream tasks. Documents are pre-processed and then iteratively annotated using a set of NLP services that combine complementary approaches based on machine learning and syntactic rules. We present a framework that has been designed to be developed and maintained in a sustainable way, allowing for multiple services and uses of the annotated document repository and considering the scarcity of annotated data as an intrinsic challenge for its development. The design activity is the result of a cooperative project where a scientific team, institutional bodies, and companies appointed to implement the final system are involved in co-design activities. We describe experiments to demonstrate the feasibility of the solution and discuss the main challenges to scaling the system at a national level. In particular, we report the results we obtained in annotating data with different low-resource methods and with solutions designed to combine these approaches in a meaningful way. An essential aspect of the proposed solution is a human-in-the-loop approach to control the output of the annotation algorithms in agreement with the organizational processes in place in Italian courts. Based on these results we advocate for the feasibility of the proposed approach and discuss the challenges that must be addressed to ensure the scalability and robustness of the proposed solution.

Automated summary

This paper presents an entity-centric infrastructure for managing legal documents, especially court judgments, based on the organization of a textual document repository and annotation. It discusses the framework's feasibility and the challenges of scaling the system nationally, as well as the importance of a human-in-the-loop approach for controlling the output of annotation algorithms.