The results show that with a limited amount of training data, TF-IDF-based models provide quality comparable to simple neural network architectures, while neural network approaches demonstrate an advantage when increasing the size of the corpus.
Parliamentary archives preserve the administrative, legal and political memory of legislative institutions. At the Senate of the Democratic Republic of the Congo, many documents are scanned PDF files, which makes manual indexing, classification and retrieval slow. This study does not propose a new algorithm. Its contri...
By using Term Frequency–Inverse Document Frequency, this study set out to analyze how different algorithms carry out the task of identifying research articles that are considered to be of similar subjects. Two algorithms were implemented in our study, one of which was a Python algorithm based on cosine similarity and t...
Recommendations are given for choosing a vectorization method depending on the data volume and system performance requirements in real time, and classical methods with linear models demonstrate competitive quality with significantly lower computational costs on small datasets.
This study evaluates the potential of large language models (LLMs) for subject indexing of Swedish LGBTQ + fiction using the dedicated QLIT thesaurus, based on Homosaurus. It examines both the quantitative performance and qualitative characteristics of AI-generated subject terms, with particular attention to thesau...
The findings confirm that combining hybrid ensemble learning with active learning offers an effective, lightweight, and cost-efficient alternative to purely transformer-based approaches, making it well-suited for real-world text classification tasks where labeled data are scarce or expensive.
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