Datasets for Portuguese Legal Semantic Textual Similarity: Comparing
  weak supervision and an annotation process approaches
Daniel da Silva Junior Paulo Roberto dos S. Corval Aline Paes Daniel de Oliveira
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.