# Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain

arXiv: https://arxiv.org/abs/2309.17171 · HTML: https://mlsanity.com/paper/2309.17171/

## Key facts

- Cited by 1 papers 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 (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains](https://mlsanity.com/paper/2106.13474/) (2021)
- [Controlling Neural Networks with Rule Representations](https://mlsanity.com/paper/2106.07804/) (2021)
- [Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity](https://mlsanity.com/paper/1706.01967/) (2017)
- [Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons](https://mlsanity.com/paper/2212.09080/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fine Tuning Named Entity Extraction Models for the Fantasy Domain](https://mlsanity.com/paper/2402.10662/) (2024)

## 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.