Hierarchical Categorization of Research Expertise in the Presence of Scarce Information

• Ano de defesa: 2018
• Autor(a) principal: Gustavo Oliveira de Siqueira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Supervised Learning; Computer Science Thesis; Machine Learning; Researchers Categorization; Aprendizado de Máquina; Computação; Recuperação de informação
• Link de acesso: http://hdl.handle.net/1843/SLSC-BBZN36

Throughout the history of science, different knowledge areas have collaborated to overcome major research challenges. The task of associating a researcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this dissertation, we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a hierarchical knowledge area classification scheme. Our proposal relies on discriminatory evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. Our experiments show that using supervised machine learning methods, trained with manually labeled information, it is possible to produce effective classification models.