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Autor:
Růžička, Michal, Sojka, Petr
In this paper, we investigate mathematical content representations suitable for the automated classification of and the similarity search in STEM documents using standard machine learning algorithms: the Latent Dirichlet Allocation (LDA) and the Late
Externí odkaz:
http://arxiv.org/abs/2110.04040
Digital mathematical libraries (DMLs) such as arXiv, Numdam, and EuDML contain mainly documents from STEM fields, where mathematical formulae are often more important than text for understanding. Conventional information retrieval (IR) systems are un
Externí odkaz:
http://arxiv.org/abs/1808.09224