Abstract
We report preliminary findings of the use of Natural Language Processing (NLP) methods for retrieving semantically similar private tax rulings (PTRs). The PTR corpus contains now more than 400 thousands of PTRs and was first described quantitively in [17]. Experiments show that use of BERT-based embeddings and cosine similarity results in very high quality set of legally similar PTRs. The quality of the set is at least on par with a manual search performed by a certified tax advisor. We also show that similar PTRs form high quality clusters that can be used for investigation of fine structure of the set. The results presented in the paper, although preliminary, already have significant practical value for tax practitioners.
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