Text Classification in Law Area: a Systematic Review
DOI:
https://doi.org/10.5753/jidm.2022.2547Keywords:
Law, Text ClassificationAbstract
This article is an extension of the KDMile 2021 accepeted submission. Automatic Text Classification represents a great improvement in law area workflow, mainly in the migration of physical to electronic lawsuits. A systematic review of studies on text classification in the legal context from January 2017 up to February 2021 was conducted. The search strategy identified 20 studies, that were analyzed and compared. The review investigates from research questions: what are the state-of-art language models (LM); LM applications on text classification in English and Brazilian Portuguese datasets from legal area; if there are available language models pre-trained on Brazilian Portuguese; and datasets from the Brazilian judicial context. It concludes that there are applications of automatic text classification in Brazil, although there is a gap on the use of language models when compared with English language dataset studies, also the importance of language model in domain pre-training to improve results, as well as there are two studies making available Brazilian Portuguese language models, and one introducing a dataset in Brazilian law area.
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