| Autor: |
Tillmann Dönicke, Hanna Varachkina, Anna Mareike Weimer, Luisa Gödeke, Florian Barth, Benjamin Gittel, Anke Holler, Caroline Sporleder |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Artificial Intelligence, Vol 4 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2624-8212 |
| DOI: |
10.3389/frai.2021.725321 |
| Popis: |
Literary narratives regularly contain passages that different readers attribute to different speakers: a character, the narrator, or the author. Since literary narratives are highly ambiguous constructs, it is often impossible to decide between diverging attributions of a specific passage by hermeneutic means. Instead, we hypothesise that attribution decisions are often influenced by annotator bias, in particular an annotator's literary preferences and beliefs. We present first results on the correlation between the literary attitudes of an annotator and their attribution choices. In a second set of experiments, we present a neural classifier that is capable of imitating individual annotators as well as a common-sense annotator, and reaches accuracies of up to 88% (which improves the majority baseline by 23%). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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