Abstract:
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 offthe-
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&Ddomain 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.
Citation:
A. Sivaganeshan and N. De Silva, "Fine Tuning Named Entity Extraction Models for the Fantasy Domain," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 346-351, doi: 10.1109/MERCon60487.2023.10355501.