Evaluating Preprocessing and Textual Representation on Brazilian Public Bidding Document Classification - A. Brandão, Michele; O. Silva, Mariana; P. Oliveira, Gabriel; Lacerda, Anisio; L. Pappa, Gisele (Journal of Information and Data Management, 2025)
In this paper, we tackle the task of classifying public bidding documents, which holds significant importance for both public and private entities seeking precise insights into bidding processes. Our study evaluates the impact of various preprocessing techniques and textual representation models, particularly word embeddings, on the accuracy of document classification. Overall, our results reveal while preprocessing techniques have minimal influence on classification outcomes, the choice of textual representation model significantly affects the representativeness of document classes. Moreover, we perform a qualitative analysis of misclassification cases, providing valuable insights into potential areas for improvement in document classification methodologies. Our findings underscore the importance of selecting appropriate textual representation models to enhance the accuracy and efficiency of document classification systems.