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dc.contributor.authorEdlin, L
dc.contributor.authorReiss, J
dc.date.accessioned2024-05-24T15:13:05Z
dc.date.available2024-05-24T15:13:05Z
dc.date.issued2023-01-01
dc.identifier.isbn9781959429920
dc.identifier.issn0736-587X
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/97050
dc.description.abstractAnimate entities in narrative comics stories are expressed through a number of visual representations across panels. Identifying these entities is necessary for recognizing characters and analysing narrative affordances unique to comics, and integrating these with linguistic reference annotation, however an annotation process for animate entity identification has not received adequate attention. This research explores methods for identifying animate entities visually in comics using annotation experiments. Two rounds of inter-annotator agreement experiments are run: the first asks annotators to outline areas on comic pages using a Polygon segmentation tool, and the second prompts annotators to assign each outlined entity’s animacy type to derive a quantitative measure of agreement. The first experiment results show that Polygon-based outlines successfully produce a qualitative measure of agreement; the second experiment supports that animacy status is best conceptualised as a graded, rather than binary, concept.en_US
dc.format.extent82 - 91
dc.publisherAssociation for Computational Linguisticsen_US
dc.titleIdentifying Visual Depictions of Animate Entities in Narrative Comics: An Annotation Studyen_US
dc.typeConference Proceedingen_US
dc.rights.holder© 2023 Association for Computational Linguistics
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderEPSRC and AHRC Centre for Doctoral Training in Media and Arts Technology::Engineering and Physical Sciences Research Councilen_US


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