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”.
|Titolo:||Combining Natural Language Processing Approaches for Rule Extraction from Legal Documents|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|