CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice
  with Fine-Grained Named Entities
Antonio Mauricio Vladia Pinheiro Vasco Furtado João Araújo Monteiro Neto Francisco das Chagas Jucá Bomfim André Câmara Ferreira da Costa Raquel Silveira Nilsiton Aragão
A basic task for most Legal Artificial Intelligence (Legal AI) applications
is Named Entity Recognition (NER). However, texts produced in the context of
legal practice make references to entities that are not trivially recognized by
the currently available NERs. There is a lack of categorization of legislation,
jurisprudence, evidence, penalties, the roles of people in a legal process
(judge, lawyer, victim, defendant, witness), types of locations (crime
location, defendant's address), etc. In this sense, there is still a need for a
robust golden collection, annotated with fine-grained entities of the legal
domain, and which covers various documents of a legal process, such as
petitions, inquiries, complaints, decisions and sentences. In this article, we
describe the development of the Golden Collection of the Brazilian Judiciary
(CDJUR-BR) contemplating a set of fine-grained named entities that have been
annotated by experts in legal documents. The creation of CDJUR-BR followed its
own methodology that aimed to attribute a character of comprehensiveness and
robustness. Together with the CDJUR-BR repository we provided a NER based on
the BERT model and trained with the CDJUR-BR, whose results indicated the
prevalence of the CDJUR-BR.