Recent advances in language representation using neural networks have made it viable to
transfer the learned internal states of a trained model to downstream natural language …

Named entity recognition systems have the untapped potential to extract information from
legal documents, which can improve information retrieval and decision-making processes …

Abstract The task of Relation Extraction is one of the main challenges in Natural Language
Processing. We present a review of the state-of-the-art for Relation Extraction in free texts …

African languages are spoken by over a billion people, but are underrepresented in NLP
research and development. The challenges impeding progress include the limited …

The exploration of legal documents in the Brazilian Judiciary context lacks reliable
annotated corpus to support the development of new Natural Language Process (NLP) …

The use of BERT, one of the most popular language models, has led to improvements in
many Natural Language Processing (NLP) tasks. One such task is Named Entity …

Digital forensics analysis is a slow process mainly due to the large amount and variety of
data. Some forensic tools help categorize files by type and allow automatization of tasks, like …

Extracting named entities text forms the basis for many crucial tasks such as information
retrieval and extraction, machine translation, opinion mining, sentiment analysis and …

One of the challenges brought on by the digital revolution of the recent decades is the
mechanism by which information carried by texts can be extracted in order to access its …

Python has a growing community of users, especially in the AI and ML fields. Yet,
Computational Processing of Portuguese in this programming language is limited, in both …