Towards Effective Data Process Pipelines for Legal NLP in English and Non-English Languages: A Greek Case Study

Natural Language Processing (NLP) pipelines form the backbone of legal artificial intelligence applications, yet most existing tools are designed for English corpora and perform poorly when transferred to morphologically rich, non-English languages. This paper investigates these limitations through a comparative study of English and Greek legal texts. It is shown that English-centric pipelines exhibit systematic errors in preprocessing (tokenization, lemmatization, stop-word removal) and fail to capture legal semantics in embeddings, resulting in degraded downstream performance. To address these issues, a generalized framework is proposed that introduces language-specific preprocessing, curated legal resources, and multilingual embeddings fine-tuned on legal corpora. A case study demonstrates how adapted tools substantially improve similarity scores and classification accuracy in Greek legal texts, while highlighting persistent challenges such as grammatical gender bias. The findings underscore the need for fairness-aware, language-specific NLP pipelines to support robust and inclusive legal AI across diverse jurisdictions.