  Cross-lingual annotations of legislative texts enable us to explore major
themes covered in multilingual legal data and are a key facilitator of semantic
similarity when searching for similar documents. Multilingual probabilistic
topic models have recently emerged as a group of semi-supervised machine
learning models that can be used to perform thematic explorations on
collections of texts in multiple languages. However, these approaches require
theme-aligned training data to create a language-independent space, which
limits the amount of scenarios where this technique can be used. In this work,
we provide an unsupervised document similarity algorithm based on hierarchies
of multi-lingual concepts to describe topics across languages. The algorithm
does not require parallel or comparable corpora, or any other type of
translation resource. Experiments performed on the English, Spanish, French and
Portuguese editions of JCR-Acquis corpora reveal promising results on
classifying and sorting documents by similar content.
