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Combining NLP Approaches for Rule Extraction from Legal Documents


Legal texts express conditions in natural language describing what is permitted, forbidden or mandatory in the context they regulate. Despite the numerous approaches tackling the problem of moving from a natural language legal text to the respective set of machine-readable conditions, results are still unsatisfiable and it remains a major open challenge. In this paper, we propose a preliminary approach which combines different Natural Language Processing techniques towards the extraction of rules from legal documents. More precisely, we combine the linguistic information provided by WordNet together with a syntax-based extraction of rules from legal texts, and a logic-based extraction of dependencies between chunks of such texts. Such a combined approach leads to a powerful solution towards the extraction of machine-readable rules from legal documents. We evaluate the proposed approach over the Australian " Telecommunications consumer protections code " .
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hal-01572443 , version 1 (07-08-2017)


  • HAL Id : hal-01572443 , version 1


Mauro Dragoni, Serena Villata, Williams Rizzi, Guido Governatori. Combining NLP Approaches for Rule Extraction from Legal Documents. 1st Workshop on MIning and REasoning with Legal texts (MIREL 2016), Dec 2016, Sophia Antipolis, France. ⟨hal-01572443⟩
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