Levels of Non-Fictionality in Fictional Texts

Florian Barth, Hanna Varachkina, Tillmann Dönicke, Luisa Gödeke

Abstract
The annotation and automatic recognition of non-fictional discourse within a text is an important, yet unresolved task in literary research. While non-fictional passages can consist of several clauses or sentences, we argue that 1) an entity-level classification of fictionality and 2) the linking of Wikidata identifiers can be used to automatically identify (non-)fictional discourse. We query Wikidata and DBpedia for relevant information about a requested entity as well as the corresponding literary text to determine the entity’s fictionality status and assign a Wikidata identifier, if unequivocally possible. We evaluate our methods on an exemplary text from our diachronic literary corpus, where our methods classify 97% of persons and 62% of locations correctly as fictional or real. Furthermore, 75% of the resolved persons and 43% of the resolved locations are resolved correctly. In a quantitative experiment, we apply the entity-level fictionality tagger to our corpus and conclude that more non-fictional passages can be identified when information about real entities is available.
Anthology ID:
2022.isa-1.4
Volume:
Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022
Month:
June
Year:
2022
Address:
Marseille, France
Editor:
Harry Bunt
Venue:
ISA
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
27–32
Language:
URL:
https://aclanthology.org/2022.isa-1.4
DOI:
Bibkey:
Cite (ACL):
Florian Barth, Hanna Varachkina, Tillmann Dönicke, and Luisa Gödeke. 2022. Levels of Non-Fictionality in Fictional Texts. In Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022, pages 27–32, Marseille, France. European Language Resources Association.
Cite (Informal):
Levels of Non-Fictionality in Fictional Texts (Barth et al., ISA 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2022.isa-1.4.pdf
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