Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works

• URL: http://arxiv.org/abs/2607.09316v1
• Date: Fri, 10 Jul 2026 11:54:02 GMT
• Title: Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works
• Abstract summary: Thematic indexing is essential to scholarly access in large-scale literary and historical editions.<n>This paper explores the application of machine learning to automatic thematic indexing.
• Score: 0.33985395340995606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Automatic Reflection Level Classification in Hungarian Student Essays [0.3262230127283452]
  We present the first comprehensive study on automatic reflection level classification in Hungarian student essays.<n>We use a large, expert-annotated Hungarian dataset consisting of 1,954 reflective essays collected over multiple academic years.<n>We investigate two approaches: classical machine learning models using TF-IDF and semantic embedding features, and Hungarian-specific transformer models fine-tuned for document-level reflection classification.
  arXiv  Detail & Related papers  (2026-05-04T09:44:50Z)
• Learning to Predict Future-Aligned Research Proposals with Language Models [59.79457676644722]
  We build a time-consistent dataset of 17,771 papers from targets and their pre-cutoff citations.<n>To train models, we build a time-consistent dataset of 17,771 papers from targets and their pre-cutoff citations.<n>Across Llama-3.1 and Qwen2.5 models, future-aligned tuning improves future alignment over unaligned baselines.
  arXiv  Detail & Related papers  (2026-03-28T05:41:15Z)
• Beyond Holistic Scores: Automatic Trait-Based Quality Scoring of Argumentative Essays [15.895792302323883]
  In educational contexts, teachers and learners require interpretable, trait-level feedback.<n>We study trait-based Automatic Argumentative Essay Scoring using two complementary modeling paradigms.<n>We show that explicitly modeling score ordinality substantially improves agreement with human raters.
  arXiv  Detail & Related papers  (2026-02-04T14:30:52Z)
• RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension [65.81339691942757]
  RPC-Bench is a large-scale question-answering benchmark built from review-rebuttal exchanges of high-quality computer science papers.<n>We design a fine-grained taxonomy aligned with the scientific research flow to assess models' ability to understand and answer why, what, and how questions in scholarly contexts.
  arXiv  Detail & Related papers  (2026-01-14T11:37:00Z)
• AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research [81.04845910798387]
  Generating natural language explanations for threat detections remains an open problem in cybersecurity research.<n>We present AutoMalDesc, an automated static analysis summarization framework that operates independently at scale.<n>We publish our complete dataset of more than 100K script samples, including annotated seed (0.9K) datasets, along with our methodology and evaluation framework.
  arXiv  Detail & Related papers  (2025-11-17T13:05:25Z)
• Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Models [0.8193467416247519]
  We introduce a purpose-oriented evaluation framework that employs nine Large Language Models (LLMs)-based metrics spanning four key dimensions of topic quality.<n>The framework is validated through adversarial and sampling-based protocols, and is applied across datasets spanning news articles, scholarly publications, and social media posts.
  arXiv  Detail & Related papers  (2025-09-08T18:46:08Z)
• Modelling and Classifying the Components of a Literature Review [0.0]
  We present a novel benchmark comprising 700 sentences manually annotated by domain experts and 2,240 sentences automatically labelled using large language models (LLMs)<n>The experiments yield several novel insights that advance the state of the art in this challenging domain.
  arXiv  Detail & Related papers  (2025-08-06T11:30:07Z)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• CiteFusion: An Ensemble Framework for Citation Intent Classification Harnessing Dual-Model Binary Couples and SHAP Analyses [1.6695303704829412]
  CiteFusion addresses the multi-class Citation Intent Classification task on two benchmark datasets: SciCite and ACL-ARC.<n>Experiments show that CiteFusion achieves state-of-the-art performance, with Macro-F1 scores of 89.60% on SciCite, and 76.24% on ACL-ARC.<n>We release a web-based application that classifies citation intents leveraging the CiteFusion models developed on SciCite.
  arXiv  Detail & Related papers  (2024-07-18T09:29:33Z)
• WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents [1.9352015147920767]
  We introduce Wilbur, an approach that uses a differentiable ranking model and a novel instruction synthesis technique. We show that our ranking model can be trained on data from a generative auto-curriculum which samples representative goals. Wilbur achieves state-of-the-art results on the WebVoyager benchmark, beating text-only models by 8% overall, and up to 36% on certain websites.
  arXiv  Detail & Related papers  (2024-04-08T23:10:47Z)
• Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification [58.720142291102135]
  This case study investigates the task of job classification in a real-world setting. The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
  arXiv  Detail & Related papers  (2023-03-13T14:09:53Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.