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.

该研究通过使用 D&D 领域的可用怪物相关知识对 Trankit 进行微调，训练系统在新的 NER 标签下从相关领域文档中提取怪物名称，并将其与零样本 Trankit 模型和两个 FLAIR 模型进行了准确性比较，结果显示经过微调的 Trankit 模型获得了 87.86% 的 F1 分数，超过其他考虑的模型。