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Contextual Tag Inference

delete2011-11-04
delete9
PRE
AI
M
Michael Mandel *
R
Razvan Pascanu
D
Douglas Eck
Y
Yoshua Bengio
L
Luca Maria Aiello
R
Rossano Schifanella
F
Filippo Menczer
DOI：10.1145/2037676.2037689delete
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Abstract

Abstract

En 中文
This article examines the use of two kinds of context to improve the results of content-based music taggers: the relationships between tags and between the clips of songs that are tagged. We show that users agree more on tags applied to clips temporally closer to one another; that conditional restricted Boltzmann machine models of tags can more accurately predict related tags when they take context into account; and that when training data is smoothed using context, support vector machines can better rank these clips according to the original, unsmoothed tags and do this more accurately than three standard multi-label classifiers.
Keywords:
Experimentation
Performance
Autotagging
clips
context
music
smoothing
tags
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

ACM Transactions on Multimedia Computing Communications and Applications cover
ACM Transactions on Multimedia Computing Communications and Applications
IF:
6
Papers:
2.0K
Citations:
5.4K

Organization

I
indiana university system
Scholars:
4.0W
Papers: 3.5W
Citations: 38
U
University of Turin
Scholars:
3.7W
Papers: 2.8W
Citations: 3.2W
U
universite de montreal
Scholars:
4.6W
Papers: 3.8W
Citations: 46
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