Comparing Nietzsche Translations with Sentence Embeddings

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I ran 5 English translations of Beyond Good and Evil through sentence embeddings to see if NLP could detect what I felt as a reader, that each translation reads like a different book.Findings:- Hollingdale sits at the semantic center, closest to the German (0.806) and to all other translators- Translators have fingerprints: UMAP separates them visually without being told who translated what- Short aphorisms diverge most, less context means more interpretive freedom- Nietzsche's pre-1901 spelling ("Werth" vs "Wert") confuses the model; built a 95-rule normalizersBuilt with MiniLM embeddings, UMAP, Next.jsCurious whether this approach could work for other translated philosophical texts, and op

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