NLP-based ontology learning from legal texts. A case study (Contributo in atti di convegno)

Type
Label
  • NLP-based ontology learning from legal texts. A case study (Contributo in atti di convegno) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • Lenci A., Montemagni S., Pirrelli V., Venturi G. (2007)
    NLP-based ontology learning from legal texts. A case study
    in II Workshop on Legal Ontologies and Artificial Intelligence Techniques (LOAIT'07), Stanford, 4 giugno 2007
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Lenci A., Montemagni S., Pirrelli V., Venturi G. (literal)
Pagina inizio
  • 113 (literal)
Pagina fine
  • 129 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In “Proceedings of the II Workshop on Legal Ontologies and Artificial Intelligence Techniques (LOAIT ’07)”, 4 June 2007, Stanford University, Stanford, CA USA, pp. 113-130. http://www.ittig.cnr.it/loait/LOAIT07-Proceedings.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Lenci A.: Università degli Studi di Pisa. Montemagni S., Pirrelli V., Venturi G.: ILC - Istituto di linguistica computazionale \"Antonio Zampolli\" (literal)
Titolo
  • NLP-based ontology learning from legal texts. A case study (literal)
Abstract
  • The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts in the environmental domain. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, are very encouraging, showing the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi-automatic extraction of legal ontologies. (literal)
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