Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain
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Abstract (arXiv)
Many NLP tasks, although well-resolved for general English, face challenges in specific domains like fantasy literature. This is evident in Named Entity Recognition (NER), which detects and categorizes entities in text. We analyzed 10 NER models on 7 Dungeons and Dragons (D&D) adventure books to assess domain-specific performance. Using open-source Large Language Models, we annotated named entities in these books and evaluated each model's precision. Our findings indicate that, without modifications, Flair, Trankit, and Spacy outperform others in identifying named entities in the D&D context.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 29 Sep 2023.
Builds on
9 of 40 references.
References · 9
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