@inproceedings{barth-etal-2022-levels,
title = "Levels of Non-Fictionality in Fictional Texts",
author = {Barth, Florian and
Varachkina, Hanna and
D{\"o}nicke, Tillmann and
G{\"o}deke, Luisa},
editor = "Bunt, Harry",
booktitle = "Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.isa-1.4",
pages = "27--32",
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.",
}
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%0 Conference Proceedings
%T Levels of Non-Fictionality in Fictional Texts
%A Barth, Florian
%A Varachkina, Hanna
%A Dönicke, Tillmann
%A Gödeke, Luisa
%Y Bunt, Harry
%S Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F barth-etal-2022-levels
%X 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.
%U https://aclanthology.org/2022.isa-1.4
%P 27-32
Markdown (Informal)
[Levels of Non-Fictionality in Fictional Texts](https://aclanthology.org/2022.isa-1.4) (Barth et al., ISA 2022)
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.