  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 Latent Semantic Indexing (LSI). The methods are evaluated on a
subset of arXiv.org papers with the Mathematics Subject Classification (MSC) as
a reference classification and using the standard precision/recall/F1-measure
metrics. The results give insight into how different math representations may
influence the performance of the classification and similarity search tasks in
STEM repositories. Non-surprisingly, machine learning methods are able to grab
distributional semantics from textual tokens. A proper selection of weighted
tokens representing math may improve the quality of the results slightly. A
structured math representation that imitates successful text-processing
techniques with math is shown to yield better results than flat TeX tokens.
