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This paper explores the automatic classification of English literary texts by epoch using the CoLiE dataset, a curated collection of texts from Project Gutenberg. The authors employ various computational approaches, including traditional bag-of-words models with TF-IDF weighting, enhancements incorporating part-of-speech n-grams, and maxLogit-based sequence classification. In addition, they examine the performance of transformer-based models such as BERT and RoBERTa, as well as the impact of named entity masking on classification accuracy. Furthermore, the study evaluates the capabilities of large language models (LLMs) in epoch detection using different prompting strategies. The results indicate that, while TF-IDF-based models achieve competitive performance, transformer-based classifiers show promise, but are occasionally outperformed. In particular, LLMs exhibit sensitivity to prompt formulation, with classification accuracy varying significantly depending on the input structure. Even in the best-case scenario, LLMs achieve accuracy approximately seven percentage points lower than other methods.

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Classification of Literary Epochs by TF-IDF, Transformers, and Large Language Models

  • 会议论文

  • 发表日期：

    2026年

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  • Tomasz Walkowiak

摘要

This paper explores the automatic classification of English literary texts by epoch using the CoLiE dataset, a curated collection of texts from Project Gutenberg. The authors employ various computational approaches, including traditional bag-of-words models with TF-IDF weighting, enhancements incorporating part-of-speech n-grams, and maxLogit-based sequence classification. In addition, they examine the performance of transformer-based models such as BERT and RoBERTa, as well as the impact of named entity masking on classification accuracy. Furthermore, the study evaluates the capabilities of large language models (LLMs) in epoch detection using different prompting strategies. The results indicate that, while TF-IDF-based models achieve competitive performance, transformer-based classifiers show promise, but are occasionally outperformed. In particular, LLMs exhibit sensitivity to prompt formulation, with classification accuracy varying significantly depending on the input structure. Even in the best-case scenario, LLMs achieve accuracy approximately seven percentage points lower than other methods.