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

• URL: http://arxiv.org/abs/2603.29801v1
• Date: Tue, 31 Mar 2026 14:32:34 GMT
• Title: ENEIDE: A High Quality Silver Standard Dataset for Named Entity Recognition and Linking in Historical Italian
• Abstract summary: ENEIDE represents the first multi-domain, publicly available NERL dataset for historical Italian with training, development, and test splits.<n>We present a methodology for semi-automatic annotations extraction from manually curated scholarly digital editions.<n> 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.
• Score: 8.815891100152148
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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