Categorização automática de produtos utilizando apenas o título e aprendizado profundo

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Paulucio, Leonardo Santos
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Mestrado em Informática
Centro Tecnológico
UFES
Programa de Pós-Graduação em Informática
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufes.br/handle/10/15983
Resumo: Natural Language Processing (NLP) has been receiving increasing attention in the past few years. In part, this is related to the huge flow of data being made available everyday on the internet, which increased the need for automatic tools capable of analyzing and extracting relevant information, especially from the text. In this context, text classification became one of the most studied tasks on the NLP domain. The objective is to assign predefined categories or labels to text or sentences. Important applications include sentence classification, sentiment analysis, spam detection, among many others. This work proposes an automatic system for product categorization using only their titles. The proposed system employs a state-of-the-art deep neural network as a tool to extract features from the titles to be used as input in different machine learning models. The system is evaluated in the large-scale Mercado Libre dataset, which has the common characteristics of real-world problems such as imbalanced classes, unreliable labels, besides having a large number of samples: 20,000,000 in total. The results showed that the proposed system was able to correctly categorize the products with a balanced accuracy of 86.57% on the local test split of the Mercado Libre dataset. It also surpassed the fourth place on the public rank of the MeLi Data Challenge with 91.19% of balanced accuracy, which represents less than 1% of the difference to the winner.