Abstract
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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Note that these ontologies are explicitly called lightweight ontologies as they are not expected to be used to normalize the concepts of legal text by mapping the legal terms into concepts in ontology, and obtain the meaning of the text by using the ontology structure. They uniquely provide a support for detecting the deontic components in legal texts and the structure of such texts, respectively.
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For more details about the meaning of each tag and dependency clauses used by the parser, please refer to the official Stanford documentation: http://nlp.stanford.edu/software/dependencies_manual.pdf.
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Dragoni, M., Villata, S., Rizzi, W., Governatori, G. (2018). Combining Natural Language Processing Approaches for Rule Extraction from Legal Documents. In: Pagallo, U., Palmirani, M., Casanovas, P., Sartor, G., Villata, S. (eds) AI Approaches to the Complexity of Legal Systems. AICOL AICOL AICOL AICOL AICOL 2015 2016 2016 2017 2017. Lecture Notes in Computer Science(), vol 10791. Springer, Cham. https://doi.org/10.1007/978-3-030-00178-0_19
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