NLP-based metadata extraction for legal text consolidation

ABSTRACT

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 meta-data. 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.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Law

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

  K. Computing Milieux
  K.5 LEGAL ASPECTS OF COMPUTING
      K.5.m Miscellaneous

General Terms:
Algorithms, Legal Aspects

Keywords:
XML representation, consolidation of legal text, metadata extraction, natural language processing, textual amendments