2023/09/29 by Gayashan Weerasundara, Nisansa de Silva, Weerasundara, Gayashan +1 · 3 voices
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling #Wikis in Education and Collaboration #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2309.17171
openalex publication_date 2023/09/29 · arxiv published 2023/09/29 · arxiv updated 2023/09/29 · openalex created_date 2023/10/03 · openalex updated_date 2026/07/28
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.