  In this article, we explore the potential of transformer-based language
models (LMs) to correctly represent normative statements in the legal domain,
taking tax law as our use case. In our experiment, we use a variety of LMs as
bases for both word- and sentence-based clusterers that are then evaluated on a
small, expert-compiled test-set, consisting of real-world samples from tax law
research literature that can be clearly assigned to one of four normative
theories. The results of the experiment show that clusterers based on
sentence-BERT-embeddings deliver the most promising results. Based on this main
experiment, we make first attempts at using the best performing models in a
bootstrapping loop to build classifiers that map normative claims on one of
these four normative theories.
