Fine Tuning Named Entity Extraction Models for the Fantasy Domain

• URL: http://arxiv.org/abs/2402.10662v1
• Date: Fri, 16 Feb 2024 13:11:13 GMT
• Title: Fine Tuning Named Entity Extraction Models for the Fantasy Domain
• Abstract summary: This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework. The system acquires the ability to extract monster names from relevant domain documents under a novel NER tag.
• Score: 0.3108011671896571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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