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In this paper, we investigate mathematical content representations suitablefor the automated classification of and the similarity search in STEM documentsusing standard machine learning algorithms: the Latent Dirichlet Allocation(LDA) and the Latent Semantic Indexing (LSI). The methods are evaluated on asubset of arXiv.org papers with the Mathematics Subject Classification (MSC) asa reference classification and using the standard precision/recall/F1-measuremetrics. The results give insight into how different math representations mayinfluence the performance of the classification and similarity search tasks inSTEM repositories. Non-surprisingly, machine learning methods are able to grabdistributional semantics from textual tokens. A proper selection of weightedtokens representing math may improve the quality of the results slightly. Astructured math representation that imitates successful text-processingtechniques with math is shown to yield better results than flat TeX tokens.

Towards Math-Aware Automated Classification and Similarity Search of Scientific Publications: Methods of Mathematical Content Representations