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This paper explores machine learning approaches, including encoder-based models and fine-tuned LLMs, for automatic thematic indexing of large literary corpora, using Voltaire's complete works as a test case. The best model achieves F1 scores up to 0.67, with implications for structured access to large-scale literary and historical editions.