NLP for D&D
ActivePI: Nisansa de Silva
This project attempts to generate adventures, images, and encounters for Dungeons and Dragons, the world's most popular Table Top Role Playing Game.
@inproceedings{weerasundara2023comparative,
title={{Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain}},
author={Weerasundara, Gayashan and de Silva, Nisansa},
booktitle={Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing},
abstract={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},
pages={1225--1233},
year={2023},
misc={https://arxiv.org/abs/2309.17171,NLP:DND,https://goo.gl/iY6aTr}
}