NLP-based Metadata Extraction for Legal Text Consolidation

The paper describes a system for the automatic consolidation of Italian legislative texts to be used as a support of an editorial consolidating activity and dealing with the following typology of textual amendments: repeal, substitution and integration. The focus of the paper is on the semantic analysis of the textual amendment provisions and the formalized representation of the amendments in terms of metadata. The proposed approach to consolidation is metadata- oriented and based on Natural Language Processing (NLP) techniques: we use XML-based standards for metadata annotation of legislative acts and a flexible NLP architecture for extracting metadata from parsed texts. An evaluation of achieved results is also provided

Towards Semantic Interpretation of Legal Modifications through Deep Syntactic Analysis

We are concerned with the automatic semantic interpretation of legal modificatory provisions. We propose a novel approach which pairs deep syntactic parsing and a fine-grained taxonomy of legal modifications. Although still in a developmental stage, the implemented system can be used to annotate with meta-information modificatory provisions of NormaInRete documents

Incomplete Innovation and the Premature Disruption of Legal Services

Article published in the Michigan State Law Review