A Comprehensive Exploitation of Instance Selection Methods for Automatic Text Classification: “Doing More with Less” - Cunha, Washington; Rocha, Leonardo; Gonçalves, Marcos A. (Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC); 2025: Anais do XXXVIII Concurso de Teses e DissertaçõesDO - 10.5753/ctd.2025.7399, 2025)
Progress in Natural Language Processing (NLP) has been dictated by the “rule of more”: more data, more computing power and more complexity, best exemplified by the current Large Language Models (LLMs). Indeed, to properly work (with high accuracy) for (domain-)specific applications, these LLMs have to be fine-tuned, i.e., trained with domain-specific data, which usually requires significant amounts of computational (and natural) resources. This Ph.D. dissertation focuses on a data engineering technique under-investigated in NLP, whose potential is enormous in the current data-intensive scenario, known as Instance Selection (IS). The IS goal is to reduce the training set size by removing noisy or redundant training instances while maintaining the effectiveness of the trained models, thus reducing the training process costs. In the PhD dissertation, we provide a comprehensive and scientifically sound comparison of many state-of-the-art (SOTA) IS methods applied to an essential NLP task – Automatic Text Classification (ATC), considering several classification solutions and many datasets. Our findings reveal a significant untapped potential for IS solutions. As a response to the limitations found in the SOTA IS methods when applied to ATC, the dissertation proposes two novel noise-oriented and redundancy-aware IS solutions specifically designed for large datasets and Transformer architectures. Our final solution achieved an average reduction of 41% in training set size while maintaining the same effectiveness levels in all experimented datasets. Our solutions demonstrated average speedup improvements of 1.67x (up to 2.46x), reducing carbon emissions (up to 65%), making them scalable for datasets with hundreds of thousands of documents. All code and datasets produced in the dissertation are available for replication on GitHub. Our results were published in some of the most important Information Retrieval and NLP conferences and journals, as it shall be detailed in this document.