Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain
This work tackles the problem of named entity recognition for researchers and practitioners in the Dungeons and Dragons domain, but it is incremental as it applies existing methods to new data.
The study evaluated 10 NER models on 7 Dungeons and Dragons adventure books to address domain-specific challenges in fantasy literature, finding that Flair, Trankit, and Spacy performed best without modifications.
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.