# Fine Tuning Named Entity Extraction Models for the Fantasy Domain

arXiv: https://arxiv.org/abs/2402.10662 · HTML: https://mlsanity.com/paper/2402.10662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [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)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [Cloze-driven Pretraining of Self-attention Networks](https://mlsanity.com/paper/1903.07785/) (2019)
- [Exploring Cross-sentence Contexts for Named Entity Recognition with BERT](https://mlsanity.com/paper/2006.01563/) (2020)
- [Lexicon Infused Phrase Embeddings for Named Entity Resolution](https://mlsanity.com/paper/1404.5367/) (2014)
- [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)
- [Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain](https://mlsanity.com/paper/2309.17171/) (2023)

## Abstract (arXiv)

Named Entity Recognition (NER) is a sequence classification Natural Language Processing task where entities are identified in the text and classified into predefined categories. It acts as a foundation for most information extraction systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with its own diverse lore. DnD entities are domain-specific and are thus unrecognizable by even the state-of-the-art off-the-shelf NER systems as the NER systems are trained on general data for pre-defined categories such as: person (PERS), location (LOC), organization (ORG), and miscellaneous (MISC). For meaningful extraction of information from fantasy text, the entities need to be classified into domain-specific entity categories as well as the models be fine-tuned on a domain-relevant corpus. This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework that uses a pre-trained model for NER. Upon this training, the system acquires the ability to extract monster names from relevant domain documents under a novel NER tag. This work compares the accuracy of the monster name identification against; the zero-shot Trankit model and two FLAIR models. The fine-tuned Trankit model achieves an 87.86% F1 score surpassing all the other considered models.