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2015 | OriginalPaper | Chapter

Practice-Oriented Evaluation of Unsupervised Labeling of Audiovisual Content in an Archive Production Environment

Authors : Victor de Boer, Roeland J. F. Ordelman, Josefien Schuurman

Published in: Research and Advanced Technology for Digital Libraries

Publisher: Springer International Publishing

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Abstract

In this paper we report on an evaluation of unsupervised labeling of audiovisual content using collateral text data sources to investigate how such an approach can provide acceptable results given requirements with respect to archival quality, authority and service levels to external users. We conclude that with parameter settings that are optimized using a rigorous evaluation of precision and accuracy, the quality of automatic term-suggestion are sufficiently high. Having implemented the procedure in our production work-flow allows us to gradually develop the system further and also assess the effect of the transformation from manual to automatic from an end-user perspective. Additional future work will be on deploying different information sources including annotations based on multimodal video analysis such as speaker recognition and computer vision.

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Footnotes
1
This data is sometimes also referred to as ‘context data’ but as for example newspaper data can also be regarded as ‘context’ we prefer the term ‘collateral data’.
 
4
http://​xtas.​net/​. Specifically, the FROG module was used using default settings.
 
5
http://​www.​cltl.​nl/​. Here the OpenNER web service was used in combination with the CLTL POS tagger.
 
7
For this non-optimized variant, recall was 21 %.
 
8
This prioritization is done by archivists independently of this work. It is in use throughout the archive and mostly determined by potential (re)use by archive clients.
 
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Metadata
Title
Practice-Oriented Evaluation of Unsupervised Labeling of Audiovisual Content in an Archive Production Environment
Authors
Victor de Boer
Roeland J. F. Ordelman
Josefien Schuurman
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-24592-8_4

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