Semantic Norm Recognition and its application to Portuguese Law
This addresses the need for better legal text interpretation for citizens, though it is incremental as it applies an existing method to a new domain-specific dataset.
The researchers tackled the problem of extracting semantic information from legal texts to help citizens understand their rights and obligations, achieving an 81.44% F1-score on a Portuguese Consumer Law corpus.
Being able to clearly interpret legal texts and fully understanding our rights, obligations and other legal norms has become progressively more important in the digital society. However, simply giving citizens access to the laws is not enough, as there is a need to provide meaningful information that cater to their specific queries and needs. For this, it is necessary to extract the relevant semantic information present in legal texts. Thus, we introduce the SNR (Semantic Norm Recognition) system, an automatic semantic information extraction system trained on a domain-specific (legal) text corpus taken from Portuguese Consumer Law. The SNR system uses the Portuguese Bert (BERTimbau) and was trained on a legislative Portuguese corpus. We demonstrate how our system achieved good results (81.44\% F1-score) on this domain-specific corpus, despite existing noise, and how it can be used to improve downstream tasks such as information retrieval.