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ENEIDE: A High Quality Silver Standard Dataset for Named Entity Recognition and Linking in Historical Italian

Cristian Santini, Sebastian Barzaghi, Paolo Sernani, Emanuele Frontoni, Laura Melosi, Mehwish Alam

Abstract

This paper introduces ENEIDE (Extracting Named Entities from Italian Digital Editions), a silver standard dataset for Named Entity Recognition and Linking (NERL) in historical Italian texts. The corpus comprises 2,111 documents with over 8,000 entity annotations semi-automatically extracted from two scholarly digital editions: Digital Zibaldone, the philosophical diary of the Italian poet Giacomo Leopardi (1798--1837), and Aldo Moro Digitale, the complete works of the Italian politician Aldo Moro (1916--1978). Annotations cover multiple entity types (person, location, organization, literary work) linked to Wikidata identifiers, including NIL entities that cannot be mapped to the knowledge graph. To the best of our knowledge, ENEIDE represents the first multi-domain, publicly available NERL dataset for historical Italian with training, development, and test splits. We present a methodology for semi-automatic annotations extraction from manually curated scholarly digital editions, including quality control and annotation enhancement procedures. Baseline experiments using state-of-the-art models demonstrate the dataset's challenge for NERL and the gap between zero-shot approaches and fine-tuned models. The dataset's diachronic coverage spanning two centuries makes it particularly suitable for temporal entity disambiguation and cross-domain evaluation. ENEIDE is released under a CC BY-NC-SA 4.0 license.

ENEIDE: A High Quality Silver Standard Dataset for Named Entity Recognition and Linking in Historical Italian

Abstract

This paper introduces ENEIDE (Extracting Named Entities from Italian Digital Editions), a silver standard dataset for Named Entity Recognition and Linking (NERL) in historical Italian texts. The corpus comprises 2,111 documents with over 8,000 entity annotations semi-automatically extracted from two scholarly digital editions: Digital Zibaldone, the philosophical diary of the Italian poet Giacomo Leopardi (1798--1837), and Aldo Moro Digitale, the complete works of the Italian politician Aldo Moro (1916--1978). Annotations cover multiple entity types (person, location, organization, literary work) linked to Wikidata identifiers, including NIL entities that cannot be mapped to the knowledge graph. To the best of our knowledge, ENEIDE represents the first multi-domain, publicly available NERL dataset for historical Italian with training, development, and test splits. We present a methodology for semi-automatic annotations extraction from manually curated scholarly digital editions, including quality control and annotation enhancement procedures. Baseline experiments using state-of-the-art models demonstrate the dataset's challenge for NERL and the gap between zero-shot approaches and fine-tuned models. The dataset's diachronic coverage spanning two centuries makes it particularly suitable for temporal entity disambiguation and cross-domain evaluation. ENEIDE is released under a CC BY-NC-SA 4.0 license.

Paper Structure

This paper contains 18 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Literary and Multilingual Corpora
  4. Italian Historical Corpora
  5. Positioning ENEIDE
  6. ENEIDE Dataset
  7. Annotation Extraction and Sampling Strategy
  8. Quality Control
  9. Dataset Statistics
  10. Experiments
  11. Named Entity Recognition
  12. Entity Linking
  13. Discussion
  14. Conclusion
  15. Limitations
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Example of markup with entities annotated for a note in Digital Zibaldone.
  • Figure 2: Example of entities annotated with HTML and RDFa in a document of the Edition of Aldo Moro's works.
  • Figure 3: Temporal distribution of named entities in the DZ corpus, showing references spanning from classical antiquity to the early modern period.
  • Figure 4: Temporal distribution of named entities in the AMD corpus, showing references concentrated in the 20th-century Italian history.