Year: 2026
Type: conference-paper
Abstract: Entity linking connects text mentions to knowledge bases.For low-resource languages, entity linking has typically not been a research priority, as named entity recognition and knowledge base creation must first be addressed.We present the first study of entity linking for Faroese, a North Germanic language with approximately 70,000 speakers.Unlike traditional systems that rely on separate candidate retrieval and ranking components, we employ an end-to-end approach using GPT-5 with integrated web search.Our method prompts the model to directly identify and link named entities to Wikipedia pages through a three-tier fallback strategy: Faroese Wikipedia, English Wikipedia, and finally any available Wikipedia.We evaluate our approach on 1,010 manually annotated examples from a Faroese NER dataset, analyzing entity mentions across Person, Location, Organization, and Miscellaneous types.Human evaluation shows our system achieves 87.5% precision and 87.3% recall, with particularly strong performance on locations (93-95% precision, 92-95% recall).Persons are more challenging (86-88% precision, 72-83% recall).The majority of links (76.5%) point to Faroese Wikipedia, demonstrating the model's ability to leverage language-specific knowledge bases.A Wikipedia API search baseline without any LLM achieves F1 = 0.57-0.60 on the same evaluation data, confirming that the LLM's contextual reasoning provides substantial gains over simple search.We validate our approach across three models (GPT-5, Gemini 3 Flash, GPT-5.4Mini), achieving F1 scores of 0.74-0.87 and confirming that the method generalizes across providers.This work establishes initial performance benchmarks for Faroese entity linking and demonstrates the viability of LLM-based approaches for low-resource languages.
Institutions: University of Iceland, University of the Faroe Islands
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166528039