# Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone

arXiv: https://arxiv.org/abs/2505.20113 · HTML: https://mlsanity.com/paper/2505.20113/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Named Entity Recognition and Classification on Historical Documents: A Survey](https://mlsanity.com/paper/2109.11406/) (2021)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [CHisIEC: An Information Extraction Corpus for Ancient Chinese History](https://mlsanity.com/paper/2403.15088/) (2024)

## Built on by (most cited first, 2 of 2)

- [It's All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models](https://mlsanity.com/paper/2601.08500/) (2026)
- [DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence](https://mlsanity.com/paper/2511.10404/) (2025)

## Abstract (arXiv)

The increased digitization of world's textual heritage poses significant challenges for both computer science and literary studies. Overall, there is an urgent need of computational techniques able to adapt to the challenges of historical texts, such as orthographic and spelling variations, fragmentary structure and digitization errors. The rise of large language models (LLMs) has revolutionized natural language processing, suggesting promising applications for Named Entity Recognition (NER) on historical documents. In spite of this, no thorough evaluation has been proposed for Italian texts. This research tries to fill the gap by proposing a new challenging dataset for entity extraction based on a corpus of 19th century scholarly notes, i.e. Giacomo Leopardi's Zibaldone (1898), containing 2,899 references to people, locations and literary works. This dataset was used to carry out reproducible experiments with both domain-specific BERT-based models and state-of-the-art LLMs such as LLaMa3.1. Results show that instruction-tuned models encounter multiple difficulties handling historical humanistic texts, while fine-tuned NER models offer more robust performance even with challenging entity types such as bibliographic references.