Detecting Spells in Fantasy Literature with a Transformer Based Artificial Intelligence

TL;DR

This paper explores using BERT, a transformer-based AI model, to identify magic spells in Harry Potter texts by fine-tuning it for context recognition, demonstrating promising transferability to other fantasy universes.

Contribution

It introduces a novel application of BERT for recognizing spells in fantasy literature and investigates the impact of sequence length and transferability across universes.

Findings

BERT can recognize spells based on context in Harry Potter.

Sequence length significantly affects model performance.

Model shows potential for transfer to other fantasy worlds.

Abstract

Transformer architectures and models have made significant progress in language-based tasks. In this area, is BERT one of the most widely used and freely available transformer architecture. In our work, we use BERT for context-based phrase recognition of magic spells in the Harry Potter novel series. Spells are a common part of active magic in fantasy novels. Typically, spells are used in a specific context to achieve a supernatural effect. A series of investigations were conducted to see if a Transformer architecture could recognize such phrases based on their context in the Harry Potter saga. For our studies a pre-trained BERT model was used and fine-tuned utilising different datasets and training methods to identify the searched context. By considering different approaches for sequence classification as well as token classification, it is shown that the context of spells can be recognised. According to our investigations, the examined sequence length for fine-tuning and validation of the model plays a significant role in context recognition. Based on this, we have investigated whether spells have overarching properties that allow a transfer of the neural network models to other fantasy universes as well. The application of our model showed promising results and is worth to be deepened in subsequent studies.