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Simone Diniz Junqueira Barbosa

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
2 author rows

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2

AILAW Journal 2019 Journal Article

Appellate Court Modifications Extraction for Portuguese

  • William Paulo Ducca Fernandes
  • Luiz José Schirmer Silva
  • Isabella Zalcberg Frajhof
  • Guilherme da Franca Couto Fernandes de Almeida
  • Carlos Nelson Konder
  • Rafael Barbosa Nasser
  • Gustavo Robichez de Carvalho
  • Simone Diniz Junqueira Barbosa

Abstract Appellate Court Modifications Extraction consists of, given an Appellate Court decision, identifying the proposed modifications by the upper Court of the lower Court judge’s decision. In this work, we propose a system to extract Appellate Court Modifications for Portuguese. Information extraction for legal texts has been previously addressed using different techniques and for several languages. Our proposal differs from previous work in two ways: (1) our corpus is composed of Brazilian Appellate Court decisions, in which we look for a set of modifications provided by the Court; and (2) to automatically extract the modifications, we use a traditional Machine Learning approach and a Deep Learning approach, both as alternative solutions and as a combined solution. We tackle the Appellate Court Modifications Extraction task, experimenting with a wide variety of methods. In order to train and evaluate the system, we have built the KauaneJunior corpus, using public data disclosed by the Appellate State Court of Rio de Janeiro jurisprudence database. Our best method, which is a Bidirectional Long Short-Term Memory network combined with Conditional Random Fields, obtained an \(F_{\beta = 1}\) score of 94. 79%.

ECAI Conference 2014 Conference Paper

Pattern-based Explanation for Automated Decisions

  • Ingrid Nunes
  • Simon Miles
  • Michael Luck
  • Simone Diniz Junqueira Barbosa
  • Carlos Lucena

Explanations play an essential role in decision support and recommender systems as they are directly associated with the acceptance of those systems and the choices they make. Although approaches have been proposed to explain automated decisions based on multi-attribute decision models, there is a lack of evidence that they produce the explanations users need. In response, in this paper we propose an explanation generation technique, which follows user-derived explanation patterns. It receives as input a multi-attribute decision model, which is used together with user-centric principles to make a decision to which an explanation is generated. The technique includes algorithms that select relevant attributes and produce an explanation that justifies an automated choice. An evaluation with a user study demonstrates the effectiveness of our approach.

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