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• W4386231894 abstract "Natural language processing (NLP) has emerged as a significant area of research within the field of artificial intelligence, receiving increased attention in recent years, which has prompted the Brazilian Ministry of Science, Technology, and Innovation to launch a project aimed at finding international funding opportunities for Brazilian researchers through its Research Financing Products Portfolio. However, the challenge of classification in this context is exacerbated by the scarcity of high-quality labeled data, which is a requirement for state-of-the-art NLP implementations. In this study, we employ machine learning strategies to classify long, unstructured, and irregular texts obtained by scraping funding institutions’ websites. Given the limited availability of labeled training data, we adopt an incremental approach to identify a suitable method with optimal performance. In order to alleviate the challenge of data scarcity, we use pre-training technology to learn word context from other data sets with significant similarities and larger scales. Then, we combine transfer learning with deep learning models to enhance sentence comprehension. We also conduct pre-processing experiments to address text irregularities. Comparative analysis with the baseline model reveals that our proposed approach yields promising results, with most trained models achieving over 90% accuracy. Our Longformer + CNN model has achieved 94% accuracy with 100% precision, while the Word2Vec + CNN model has achieved 93.55% accuracy. These findings highlight the successful application of artificial intelligence in public administration." @default.
• W4386231894 created "2023-08-29" @default.
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• W4386231894 date "2023-01-01" @default.
• W4386231894 modified "2023-10-01" @default.
• W4386231894 title "Leveraging Transfer Learning for Long Text Classification with Limited Data" @default.
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• W4386231894 doi "https://doi.org/10.1007/978-3-031-43088-6_6" @default.
• W4386231894 hasPublicationYear "2023" @default.
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