# Semantic Norm Recognition and its application to Portuguese Law

arXiv: https://arxiv.org/abs/2203.05425 · HTML: https://mlsanity.com/paper/2203.05425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Merge and Label: A novel neural network architecture for nested NER](https://mlsanity.com/paper/1907.00464/) (2019)
- [Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators](https://mlsanity.com/paper/1810.09073/) (2018)
- [Information Extraction Framework to Build Legislation Network](https://mlsanity.com/paper/1812.01567/) (2018)

## Abstract (arXiv)

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.