  In this work, we analyze the utility of two dimensional document maps for
exploratory analysis of Polish case law. We start by comparing two methods of
generating such visualizations. First is based on linear principal component
analysis (PCA). Second makes use of the modern nonlinear t-Distributed
Stochastic Neighbor Embedding method (t-SNE). We apply both PCA and t-SNE to a
corpus of judgments from different courts in Poland. It emerges that t-SNE
provides better, more interpretable results than PCA. As a next test, we apply
t-SNE to randomly selected sample of common court judgments corresponding to
different keywords. We show that t-SNE, in this case, reveals hidden topical
structure of the documents related to keyword,,pension". In conclusion, we find
that the t-SNE method could be a promising tool to facilitate the exploitative
analysis of legal texts, e.g., by complementing search or browse functionality
in legal databases.
