The Detection and Understanding of Fictional Discourse
This work addresses the challenge of enriching cultural heritage archives and understanding fictional storytelling, but it appears incremental as it applies existing methods to new data without major breakthroughs.
The paper tackled the problem of detecting fictional discourse by conducting classification experiments on diverse datasets including published fiction, fanfiction, and GPT-generated stories, achieving results that help identify distinctive qualities of fictional storytelling.
In this paper, we present a variety of classification experiments related to the task of fictional discourse detection. We utilize a diverse array of datasets, including contemporary professionally published fiction, historical fiction from the Hathi Trust, fanfiction, stories from Reddit, folk tales, GPT-generated stories, and anglophone world literature. Additionally, we introduce a new feature set of word "supersenses" that facilitate the goal of semantic generalization. The detection of fictional discourse can help enrich our knowledge of large cultural heritage archives and assist with the process of understanding the distinctive qualities of fictional storytelling more broadly.