Named Entity Recognition: a Survey for the Portuguese Language

Named Entity Recognition (NER) is an important task in Natural Lan-guage Processing, as it is a key information extraction sub-task with numerous ap-plications, such as information retrieval and machine learning. However, resources are still scarce for some languages, as it is the case of Portuguese. Thus, the objective of this research is to map NER techniques, methods and resources for the Portugue-se language. Manual and automated searches were applied, retrieving 447 primary studies, of which 45 were included in our review. The growing number of studies reveal a greater interest of researchers in the area. 21 studies focused on the compa-rative analysis between techniques and tools. 24 new or updated NER corpora were mapped, in several domains. The most used text pre-processing techniques were to-kenization, embeddings, and PoS Tagging, while the most used methods/algorithms were based on BiLSTM, CRF, and BERT models. The most relevant researchers, institutions and countries were also mapped, as well as the evolution of publications.

Automated summary

Named Entity Recognition (NER) is a crucial task in Natural Language Processing, with various applications and research interest. However, resources for languages like Portuguese are lacking. This study aims to map NER techniques and resources for Portuguese. A total of 447 primary studies were retrieved, and 45 were included in the review. Comparative analysis, new corpora, and commonly used techniques and algorithms were identified.