Identifying named entity from researcher curricula - Gonçalves, Rodrigo; Dorneles, Carina F. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2022: Anais do XXXVII Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2022.226233, 2022)
NER (Named Entity Recognition) is an essential task in recognizing real-world entities scattered in a document. The task has been beneficial for detecting people, institutions, and places. In a researcher's curriculum repository, a NER process can be beneficial for understanding the associated context of a given document. For example, it could be possible to identify which persons/institutions are present in a given researcher's curriculum. This process is fundamental to identifying experts to work on a project or collaboration among researchers. In this paper, we evaluate entity extraction methods' effectiveness for identifying entities from scientific publications, including vocabulary-based and model-based methods. We describe an analysis of existing NER tools while proposing a procedure to apply NER identification over curricula from the Brazilian Lattes Curricula platform.