Musical Heritage Historical Entity Linking
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Abstract (arXiv)
Linking named entities occurring in text to their corresponding entity in a Knowledge Base (KB) is challenging, especially when dealing with historical texts. In this work, we introduce Musical Heritage named Entities Recognition, Classification and Linking (MHERCL), a novel benchmark consisting of manually annotated sentences extrapolated from historical periodicals of the music domain. MHERCL contains named entities under-represented or absent in the most famous KBs. We experiment with several State-of-the-Art models on the Entity Linking (EL) task and show that MHERCL is a challenging dataset for all of them. We propose a novel unsupervised EL model and a method to extend supervised entity linkers by using Knowledge Graphs (KGs) to tackle the main difficulties posed by historical documents. Our experiments reveal that relying on unsupervised techniques and improving models with logical constraints based on KGs and heuristics to predict NIL entities (entities not represented in the KB of reference) results in better EL performance on historical documents.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 72 references that are in the map. First posted on arXiv 13 Feb 2025.
Builds on
23 of 72 references.
References · 23
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11 more
- 2021Multilingual Autoregressive Entity Linkingcited by 50
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- 2021Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLPcited by 30
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- 2023Learn to Not Link: Exploring NIL Prediction in Entity Linkingcited by 7
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Citing papers per year: 2026: 2.
2 papers.