Named entity recognition and linking in PoeTree corporaPlecháč, Petr (Avtor) Šeļa, Artjoms (Avtor) Cinková, Silvie (Avtor) De Sisto, Mirella (Avtor) Nugues, Lara (Avtor) Kočnik, Neža (Avtor) Kolár, Robert (Avtor) Haider, Thomas (Avtor) poetrynamed entitiescomputational poeticsnatural language processingNamed entity recognition (NER) and named entity linking (NEL) remain underexplored in poetic texts. This study provides the first large-scale evaluation of contemporary NER and NEL systems on poetry across seven European languages – Czech, German, English, French, Italian, Russian, and Slovenian – using corpora from the PoeTree project. We benchmark three NER systems (flair, NameTag 2, spaCy) and three GPT models (GPT-3.5, GPT-4, GPT-4 Turbo) against manually annotated gold standards. While results fall short of in-domain benchmarks, they significantly outperform earlier findings. Manual correction further raises final annotation quality to estimated F1 scores between 0.77 and 0.93 across languages. We additionally evaluate two NEL systems – spaCy fishing and mGenre – showing that mGenre consistently outperforms spaCy fishing, achieving in-KB F1-scores of 0.70–0.81. By analysing geographic distances between predicted and gold-standard links, we demonstrate that a substantial portion of “incorrect” predictions are near-miss ambiguities rather than substantive errors. The resulting manually verified geolocation annotations have been integrated into PoeTree and made available through an interactive map interface.University of Tartu Press20252026-05-14 03:10:04Znanstveno delo98082UDK: 82COBISS_ID: 277968387DOI: 10.12697/smp.2025.12.2.01ISSN pri članku: 2346-691Xsl