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Bibliographic Details
Main Authors: Piper, Andrew, Zhou, Haiqi
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2401.16678
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author Piper, Andrew
Zhou, Haiqi
author_facet Piper, Andrew
Zhou, Haiqi
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_16678
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The Detection and Understanding of Fictional Discourse
Piper, Andrew
Zhou, Haiqi
Computation and Language
Machine Learning
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.
title The Detection and Understanding of Fictional Discourse
topic Computation and Language
Machine Learning
url https://arxiv.org/abs/2401.16678