Beyond Six Digits: Automated Tariff Line HS Transposition Using Natural Language Processing

This paper explores the application of Natural Language Processing (NLP) techniques to automate Harmonized System (HS) tariff line transposition, employing a three-stage process: unique 1:1 tariff code matching (Round 1), exact description matching (Round 2), and “smart” description matching (Round 3) using Artificial Intelligence (AI) and lexical similarity methods paired with harmonized 6-digit concordance and cosine similarity. Similarity is calculated using either Term Frequency Inverse Document Frequency (TF-IDF) vectors or Sentence-BERT (SBERT) embeddings, comparing two scenarios: a straightforward case (Economy A) with standardized descriptions, and a complex case (Economy B), with more detailed technical descriptions.