  Recent advances in Artificial Intelligence (AI) have leveraged promising
results in solving complex problems in the area of Natural Language Processing
(NLP), being an important tool to help in the expeditious resolution of
judicial proceedings in the legal area. In this context, this work targets the
problem of detecting the degree of similarity between judicial documents that
can be achieved in the inference group, by applying six NLP techniques based on
the transformers architecture to a case study of legal proceedings in the
Brazilian judicial system. The NLP transformer-based models, namely BERT, GPT-2
and RoBERTa, were pre-trained using a general purpose corpora of the Brazilian
Portuguese language, and then were fine-tuned and specialised for the legal
sector using 210,000 legal proceedings. Vector representations of each legal
document were calculated based on their embeddings, which were used to cluster
the lawsuits, calculating the quality of each model based on the cosine of the
distance between the elements of the group to its centroid. We noticed that
models based on transformers presented better performance when compared to
previous traditional NLP techniques, with the RoBERTa model specialised for the
Brazilian Portuguese language presenting the best results. This methodology can
be also applied to other case studies for different languages, making it
possible to advance in the current state of the art in the area of NLP applied
to the legal sector.

Artificial Intelligence;Computation and Language;Machine Learning