Abstract
Machine learning (ML) has significantly advanced text classification by enabling automated understanding and categorization of complex, unstructured textual data. However, accurately capturing nuanced linguistic patterns and contextual variations inherent in natural language, particularly within consumer complaints, remains a challenge. This study addresses these issues by incorporating human-experience-trained algorithms that effectively recognize subtle semantic differences crucial for assessing consumer relief eligibility. Furthermore, we propose integrating synthetic data generation methods that utilize expert evaluations of generative adversarial networks and are refined through expert annotations. By combining expert-trained classifiers with high-quality synthetic data, our research seeks to significantly enhance machine learning classifier performance, reduce dataset acquisition costs, and improve overall evaluation metrics and robustness in text classification tasks.
Abstract (translated)
机器学习（ML）通过实现对复杂、非结构化文本数据的自动理解和分类，在文本分类方面取得了显著进展。然而，准确捕捉自然语言中细微的语言模式和上下文变化，特别是在消费者投诉中的问题，仍然是一项挑战。本研究通过引入基于人类经验训练的算法来解决这些问题，这些算法能够有效识别评估消费者救济资格时至关重要的细微语义差异。 此外，我们还提议整合生成合成数据的方法，利用专家对生成对抗网络（GAN）的评价，并通过专家注释进行改进。通过结合由专家培训的分类器和高质量的合成数据，我们的研究旨在显著提高机器学习分类器的表现，减少数据集获取成本，并改善文本分类任务的整体评估指标和鲁棒性。
URL
https://arxiv.org/abs/2506.21623
