Comparative Analysis of Named Entity Recognition in the Dungeons and
  Dragons Domain
Gayashan Weerasundara Nisansa de Silva
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.