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The thematic modelling of subtext

The thematic modelling of subtext
The thematic modelling of subtext
Narratives form a key component of multimedia knowledge representation on the Web. However, many existing multimedia narrative systems either ignore the narrative qualities of any media, or focus on the literal depicted content ignoring any subtext. Ignoring narrative subtext can lead to erroneous search results, or automatically remixed content that lacks cohesion. We suggest that subtext can be computationally modeled in terms of Tomashevsky's hierarchy of themes and motifs. These elements can then be used in a semiotic term expansion algorithm, incorporating knowledge of subtext into search and subsequent narrative generation. We present two experimental applications of this technique. In the first, we use our thematic model in the automatic construction of photo montages from Flickr, comparing it to more traditional term expansion based on co-occurrence, and showing that this improves the perceived relevance of images within the montage. In the second, we use the thematic model in order to automatically identify Flickr images to illustrate short stories, where it dampened the perception of unwanted themes (an effect we describe as reducing thematic noise). Our work is among the fi rst in this space, and shows that thematic subtext can be tackled computationally.
Narrative, Term Expansion, Thematics, Narrative Cohesion, Semiotics, Multimedia Mining
1380-7501
Hargood, Charles
3c8d84a9-7742-4de4-8dd6-a46f6617d290
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4
Hargood, Charles
3c8d84a9-7742-4de4-8dd6-a46f6617d290
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4

Hargood, Charles, Millard, David and Weal, Mark (2018) The thematic modelling of subtext. Multimedia Tools and Applications. (doi:10.1007/s11042-018-5972-y).

Record type: Article

Abstract

Narratives form a key component of multimedia knowledge representation on the Web. However, many existing multimedia narrative systems either ignore the narrative qualities of any media, or focus on the literal depicted content ignoring any subtext. Ignoring narrative subtext can lead to erroneous search results, or automatically remixed content that lacks cohesion. We suggest that subtext can be computationally modeled in terms of Tomashevsky's hierarchy of themes and motifs. These elements can then be used in a semiotic term expansion algorithm, incorporating knowledge of subtext into search and subsequent narrative generation. We present two experimental applications of this technique. In the first, we use our thematic model in the automatic construction of photo montages from Flickr, comparing it to more traditional term expansion based on co-occurrence, and showing that this improves the perceived relevance of images within the montage. In the second, we use the thematic model in order to automatically identify Flickr images to illustrate short stories, where it dampened the perception of unwanted themes (an effect we describe as reducing thematic noise). Our work is among the fi rst in this space, and shows that thematic subtext can be tackled computationally.

Text IJMTA-hargood - Accepted Manuscript
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More information

Accepted/In Press date: 10 April 2018
e-pub ahead of print date: 30 April 2018
Keywords: Narrative, Term Expansion, Thematics, Narrative Cohesion, Semiotics, Multimedia Mining

Identifiers

Local EPrints ID: 419730
URI: https://eprints.soton.ac.uk/id/eprint/419730
ISSN: 1380-7501
PURE UUID: 20d2b89e-7609-455e-bff2-d1fba06d10d3
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710
ORCID for Mark Weal: ORCID iD orcid.org/0000-0001-6251-8786

Catalogue record

Date deposited: 20 Apr 2018 16:30
Last modified: 14 May 2018 16:30

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Contributors

Author: Charles Hargood
Author: David Millard ORCID iD
Author: Mark Weal ORCID iD

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