Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works
This paper explores machine learning for automatic thematic indexing of large literary corpora, using Voltaire's works as a test case. The best model, a 4-bit quantized Mistral, achieves F1 scores up to 0.67, highlighting the potential of automated indexing.
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[Submitted on 10 Jul 2026]
Title:Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works
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Abstract:Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process. This paper explores the application of machine learning to automatic thematic indexing, using two substantial sub-corpora of the Complete Works of Voltaire as a test case: the Essai sur les mœurs et l'esprit des nations and the Questions sur l'Encyclopédie. The task is framed as a multi-label classification problem, in which a model must assign the set of index entries that a professional indexer would apply to a given page of text. We compare a range of approaches -- from encoder-based models with classification heads to generative large language models (LLMs) fine-tuned via Low-Rank Adaptation (LoRA) -- spanning model sizes from approximately 3 to 120 billion parameters. Our best-performing model, from the Mistral family in a 4-bit quantised configuration, achieves F1 scores of up to 0.67; we argue that these figures represent lower bounds, given the inherent subjectivity of professional indexing and the frequency with which model predictions prove semantically valid despite diverging from the print index. We further evaluate cross-corpus generalisation and conduct a detailed qualitative analysis of model behaviour on literary and rhetorical features of the source texts that prove particularly resistant to automated treatment. Our findings have implications for the broader challenge of providing structured thematic access to large-scale literary and historical corpora.
Comments: 22 pages, 3 figures, 3 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2607.09316 [cs.CL]
(or arXiv:2607.09316v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09316
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Miguel Arana-Catania [view email] [v1] Fri, 10 Jul 2026 11:54:02 UTC (576 KB)
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