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
- Venue:
- ISA
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 27–32
- Language:
- URL:
- https://aclanthology.org/2022.isa-1.4
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.isa-1.4.pdf
- Data
- DBpedia