Entity Extraction from Portuguese Legal Documents Using Distant Supervision

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


  • Lucas M. Navarezi
  • Kenzo Sakiyama
  • Lucas S. Rodrigues
  • Caio M.O. Robaldo
  • Gustavo R. Lobato
  • Paulo A. Vilela
  • Edson T. Matsubara
  • Eraldo R. Fernandes

Most approaches to role-filler entity extraction (REE) rely on large labeled training corpora in which entity mentions are directly annotated in the input document. In this work, we leverage an existing knowledge base (KB) of entities to perform document-level REE from drug seizure petitions. We propose a system that learns to extract entities from petitions to fill 29 roles of a drug seizure event. Although we have access to a KB covering more than 170 thousand entities and six thousand petitions, such that each entity in the KB is linked to a specific petition, the mentions to an entity within a petition’s text are not annotated. The lack of these annotations brings challenges related to mismatches between entity values in the KB and entity mentions in the documents. Additionally, there are entities with same type or same value. Thus, we propose a distant annotation method to overcome these challenges and automatically label petition documents using the available KB. This annotation method includes a parameter that controls the balance between precision and recall. We also propose a strategy to effectively tune this parameter in order to optimize a given metric. We then train a BERT-based sequence labeling model that learns to identify entity mentions and label them. Our system achieves an F1 score of 78.59 with precision over 82%. We also report ablation studies regarding the distant annotation method.

Original languageEnglish
Title of host publicationComputational Processing of the Portuguese Language : 15th International Conference, PROPOR 2022, Fortaleza, Brazil, March 21-23, 2022, Proceedings
EditorsVládia Pinheiro, Pablo Gamallo, Raquel Amaro, Carolina Scarton, Fernando Batista, Diego Silva, Catarina Magro, Hugo Pinto
Number of pages11
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Publication date2022
ISBN (print)978-3-030-98304-8
ISBN (electronic)978-3-030-98305-5
Publication statusPublished - 2022
Externally publishedYes
Event15th International Conference on the Computational Processing of Portuguese - PROPOR 2022 - University of Fortaleza / hybrid, Fortaleza, Brazil
Duration: 21.03.202223.03.2022