Fine Tuning Named Entity Extraction Models for the Fantasy Domain
<|reference_start|>Fine Tuning Named Entity Extraction Models for the Fantasy Domain: 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.<|reference_end|>
@article{sivaganeshan2024fine,
    title={Fine Tuning Named Entity Extraction Models for the Fantasy Domain},
    author={Aravinth Sivaganeshan, Nisansa de Silva},
    journal={arXiv preprint arXiv:2402.10662},
    year={2024},
    doi={10.1109/MERCon60487.2023.10355501},
    archivePrefix={arXiv},
    eprint={2402.10662},
    primaryClass={cs.CL}
}