# Datasets for Portuguese Legal Semantic Textual Similarity: Comparing weak supervision and an annotation process approaches

arXiv: https://arxiv.org/abs/2306.00007 · HTML: https://mlsanity.com/paper/2306.00007/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 1 of 1)

- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)

## Built on by (most cited first, 3 of 3)

- [Check-Eval: A Checklist-based Approach for Evaluating Text Quality](https://mlsanity.com/paper/2407.14467/) (2024)
- [JurisTCU: A Brazilian Portuguese Information Retrieval Dataset with Query Relevance Judgments](https://mlsanity.com/paper/2503.08379/) (2025)
- [BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, including case law](https://mlsanity.com/paper/2505.15916/) (2025)

## Abstract (arXiv)

The Brazilian judiciary has a large workload, resulting in a long time to finish legal proceedings. Brazilian National Council of Justice has established in Resolution 469/2022 formal guidance for document and process digitalization opening up the possibility of using automatic techniques to help with everyday tasks in the legal field, particularly in a large number of texts yielded on the routine of law procedures. Notably, Artificial Intelligence (AI) techniques allow for processing and extracting useful information from textual data, potentially speeding up the process. However, datasets from the legal domain required by several AI techniques are scarce and difficult to obtain as they need labels from experts. To address this challenge, this article contributes with four datasets from the legal domain, two with documents and metadata but unlabeled, and another two labeled with a heuristic aiming at its use in textual semantic similarity tasks. Also, to evaluate the effectiveness of the proposed heuristic label process, this article presents a small ground truth dataset generated from domain expert annotations. The analysis of ground truth labels highlights that semantic analysis of domain text can be challenging even for domain experts. Also, the comparison between ground truth and heuristic labels shows that heuristic labels are useful.